AI Safety as a Global Public Good: Carnegie Endowment Report Analysis

Why AI Safety Needs a Global Public Good Framework

As artificial intelligence systems grow more powerful and deeply integrated into society, their safe development presents one of the most critical governance challenges of our generation. The landmark report “Examining AI Safety as a Global Public Good”, published by the Carnegie Endowment for International Peace in collaboration with Concordia AI and the Oxford Martin AI Governance Initiative, offers a rigorous framework for understanding why current approaches to AI safety may be insufficient — and how rethinking the problem through the lens of global public goods could unlock new paths to coordination.

Written by sixteen scholars from institutions spanning five continents — including researchers from Stanford, Oxford, Cambridge, Tsinghua University, and the Future of Life Institute — the report draws on lessons from climate change governance, nuclear safety, and global health to examine whether and how the “public good” framework can help us better understand and address the challenges posed by advanced AI systems. The analysis arrives at a critical moment when the pace of AI capability development increasingly outstrips the development of safety measures and governance mechanisms.

The central premise is straightforward yet profound: if AI safety measures, knowledge, and practices are genuinely beneficial to all of humanity, they should be treated as global public goods — goods that are non-rivalrous (one party’s use doesn’t diminish another’s) and non-excludable (no one can be prevented from benefiting). This framing carries significant implications for how governments, corporations, and international institutions should approach the development, sharing, and funding of AI safety research. It also intersects with the broader governance challenges documented in the Stanford AI Index Policy Governance 2025 report.

How AI Safety Qualifies as a Public Good

The report applies the classical economics definition of a public good to AI safety with careful precision. A public good must satisfy two criteria: non-rivalry (consumption by one party doesn’t reduce availability to others) and non-excludability (it is impractical to prevent anyone from accessing the benefit). The analysis demonstrates that AI safety meets both criteria in important ways.

Non-excludability: Safety measures, once developed and implemented, naturally extend their protective benefits beyond the immediate jurisdiction or entity that created them. Advances in interpretability techniques or robustness measures generate knowledge that ultimately influences practices across the entire AI safety field. Open-access preprints and open-source innovations have historically been norms in this community, making safety knowledge inherently difficult to contain.

Non-rivalry: Safety protocols benefit all users equally. Standards improve with wider adoption, and safety knowledge grows more valuable with diverse inputs. Unlike rival goods where consumption reduces supply, a robustness testing framework becomes more effective — not less — as more organizations adopt and contribute to it.

Critically, the report identifies the underprovision risk that defines classic public good failures. Markets systematically under-provide AI safety because the benefits are public while the costs are private. Individual companies cannot fully capture the returns on their safety investments, while competitors who invest less in safety can benefit as free-riders from the broader ecosystem’s safety improvements. This dynamic mirrors the atmospheric commons problem in climate change, where individual nations have insufficient incentives to reduce emissions unilaterally.

The analysis identifies three interconnected layers of AI public goods: the technical/built level (open-source evaluation tools, testing frameworks, safety protocols), the knowledge level (scientific understanding and governance capabilities), and the institutional level (governance frameworks and capacity development infrastructure). Each layer presents distinct challenges for provision and access.

Lessons from Climate Change Governance

The report draws extensively on climate change governance as the most developed analogy for global AI safety coordination. The parallel is compelling: the benefits of climate stability, like the benefits of AI safety, are shared by all; the atmosphere is inherently shared; and mitigation efforts help everyone regardless of who bears the costs. The underprovision problem in both domains arises because individual actors — whether nations or corporations — face insufficient incentives to invest optimally in the collective good.

The most instructive lesson from climate governance is the principle of “common but differentiated responsibilities” established during UNFCCC negotiations. Developing nations feared that uniform emissions restrictions would hamper their industrialization, leading to a framework that acknowledged different national capacities and historical responsibilities. The report identifies a direct parallel in AI safety: developing nations may view uniform safety requirements as barriers to their own AI development aspirations, and any effective governance framework must account for these asymmetries.

However, the climate analogy also carries cautionary lessons. Postcolonial critiques demonstrate how seemingly neutral international frameworks can function as “sophisticated mechanisms of economic and normative control.” The Paris Agreement process revealed how powerful states pushed to reduce legally binding mitigation measures that were of particular importance to smaller, more immediately at-risk nations. For AI safety governance, this suggests that any framework claiming to serve all humanity must be designed with explicit safeguards against reinforcing existing power hierarchies — a challenge explored further in the America’s AI Action Plan 2025 analysis.

Nuclear Safety and Global Health Parallels

Beyond climate change, the report examines two additional governance domains with instructive parallels for AI safety. Nuclear safety developed primarily through national regulatory frameworks before scaling to international coordination — a graduated approach that the authors suggest could model AI safety governance. The Three Mile Island incident drove domestic safety improvements in the United States before contributing to international standards. This “local first” pathway demonstrates how national frameworks can serve as building blocks for eventual global governance.

Global health governance, particularly pandemic prevention, offers the most direct parallel to AI safety through what economists call the “weakest link” model. Just as disease control requires comprehensive global surveillance because contagions cross borders regardless of national measures, AI safety requires attention to the weakest points in the global development ecosystem. Unsafe AI development in any jurisdiction can generate risks for all nations, regardless of how responsibly other jurisdictions behave.

The COVID-19 pandemic illustrated both the potential and the limits of the public goods framework. Medical knowledge functioned as a genuine public good — openly shared scientific understanding accelerated vaccine development globally. Yet critical medical supplies and equitable vaccine distribution were dramatically under-provided, illustrating the classic gap between knowledge as a public good and implementation as a coordination challenge. Aviation safety offers another model, showing how local and national concerns about specific carriers evolved into robust international frameworks for aircraft certification, confirming the graduated approach pattern.

The Safety-Capability Paradox

Perhaps the most intellectually challenging finding in the report is what it terms the “safety-capability paradox” — the fundamental tension between advancing AI safety and advancing AI capabilities. Some critical AI safety measures may simultaneously advance capabilities, and some safety measures may require advanced capabilities to implement. This creates a governance dilemma with no easy resolution.

Improvements in model interpretability, for example, might simultaneously enhance safety oversight AND enable more sophisticated — and potentially riskier — AI applications. Sharing safety evaluation frameworks globally could inadvertently accelerate capability development in ways that raise security concerns. Countries might rationally choose to keep safety tools private to maintain technological advantages, even though broader sharing would make AI safer for everyone.

The report identifies safety work that can be credibly separated from capability advancement: monitoring and oversight tools, testing protocols, organizational safety measures, transparency tools, and basic safety architecture elements. However, other domains are tightly coupled with capability advancement: technical alignment research, evaluation frameworks requiring sophisticated AI reasoning, reliability improvements, and scalable oversight mechanisms. This distinction is critical for governance design — sharing some safety advances may be straightforward, while sharing others requires carefully designed mechanisms that protect against dual-use risks, similar to those documented in the CISA Artificial Intelligence program.

An additional risk identified in the report is “safety-washing” — where actors use safety-based language to describe advances that are primarily about capabilities. This undermines the credibility of the entire global public goods approach and requires reliable metrics and auditing mechanisms to distinguish genuine safety improvements from capability advances wrapped in safety rhetoric.

Accountability vs. Collective Responsibility

The report identifies a fundamental tension at the heart of the global public good framework: the need to balance collective responsibility with targeted accountability. AI safety requires broad international cooperation, but this must not diminish the accountability of leading AI developers and the small number of states that possess disproportionate power and leverage in AI development.

The stark disparity between nations developing frontier AI and nations primarily implementing AI created elsewhere constrains meaningful global cooperation to a relatively small number of key decision-makers. The global public goods framing potentially risks diluting focused accountability — in current discourse, AI safety is reasonably framed as primarily the responsibility of advanced AI organizations and leading AI-developing states. Broadening the frame to “everyone’s responsibility” could paradoxically reduce the pressure on those with the greatest capacity and obligation to act.

Free-rider dynamics compound this challenge. Individual actors may underinvest in safety measures knowing they can benefit from others’ investments without bearing costs. When multiple actors adopt this approach, collective investment falls below socially optimal levels. The report notes that the global public goods framing might also inadvertently discourage private sector investment in safety research, as companies would be expected to share advances freely while bearing the full development costs — a dynamic that must be addressed through carefully designed incentive structures. This accountability question connects to broader governance debates in the OECD AI Policy Observatory framework.

Global Equity and the AI Development Divide

The report devotes significant attention to what may be the most politically sensitive dimension of AI safety governance: global equity. The asymmetry between the small number of nations driving AI development and the global scope of potential impacts creates complex governance challenges that mirror — and risk deepening — existing global inequalities.

Developing nations face a double bind. On one hand, AI safety requirements may be perceived as potential barriers to their own AI development aspirations. Speed of implementation could provide competitive economic benefits that outweigh the costs of voluntarily focusing on safety. On the other hand, without immediate representation in AI development and safety frameworks, these nations risk being locked out of both near-term benefits and long-term governance decisions — potentially deepening existing global inequalities.

The report’s engagement with postcolonial critiques is particularly valuable. The terminology of “global public goods” itself, the authors acknowledge, often reinforces existing geopolitical hierarchies despite intentions of promoting collective responsibility. Such frameworks can be invoked within international treaty mechanisms that enforce uniform responsibility for a problem that was unequally created. Framing AI safety in market failure terms rather than a more rights-based lens may shift attention away from structural inequities and restrict the state’s role to market facilitator rather than rights guarantor.

The proposed solutions center on incorporating capacity building and knowledge transfer mechanisms into any global governance framework, ensuring AI safety requirements don’t unduly constrain development goals, developing governance mechanisms that avoid perpetuating existing inequities, and enabling meaningful participation from all affected communities. These recommendations connect to the broader digital inclusion agenda documented in the World Economic Forum Global Cybersecurity Outlook 2025 analysis.

Governance Models for AI Safety Provision

The report outlines three distinct models for providing AI safety as a global public good, each suited to different aspects of the challenge. The Aggregate Efforts model involves coordinated contributions across stakeholders for cumulative impact — collaborative development of safety assessment standards, requiring broad participation to avoid “regulatory flight” where developers move to jurisdictions with less burdensome rules.

The Weakest Link model prioritizes addressing vulnerabilities at the global system’s weakest points — establishing basic safety capabilities and monitoring systems in all jurisdictions where AI development or deployment occurs, ensuring that no region becomes a weak point that undermines global safety. The Single Best-Shot model concentrates resources on breakthrough solutions that benefit all stakeholders — focusing on key technical challenges like formal verification methods and robustness techniques while ensuring global distribution of results.

The report advocates for a graduated, multi-level approach that builds on AI safety work at local, regional, and global levels while developing scalable governance mechanisms held in common across those levels. This “local first” approach leverages existing governance capabilities while avoiding the dilution of responsibility that often accompanies premature internationalization. The approach must bridge the gap between nations with significant AI development capabilities and those primarily managing AI’s impacts — a challenge that requires both institutional innovation and sustained political commitment.

An intriguing concept from the analysis is that of “privileged groups” — states with sufficient resources and incentives to provide AI safety measures regardless of others’ participation. Major AI-developing nations might contribute to global AI safety even while acting independently, though the report cautions that unilateral decision-making may still induce competitive racing dynamics. Effective governance must harness this willingness to lead while channeling it toward genuinely collaborative outcomes.

Research Priorities and Future Agenda

The report outlines a comprehensive research agenda spanning six categories that will be essential for translating the public goods framework into practical governance. The core universal needs category asks fundamental questions: how to identify and prioritize potential global risks from advanced AI requiring collective action, which aspects of human agency are most threatened, and what minimum safety requirements must be universally guaranteed to prevent catastrophic outcomes.

The elements of AI safety category examines how specific safety components — robustness measures, testing frameworks, transparency tools, governance standards — function as public goods at different levels. A critical question is whether there are cases where safety benefits are excludable yet safety failures have non-excludable consequences — a pattern that would require different governance approaches than pure public goods.

The governance mechanisms category addresses the most practical challenges: developing verification mechanisms that enable sharing of safety advances while protecting security interests, facilitating meaningful public debate while acknowledging power asymmetries, and designing governance structures that avoid perpetuating inequities. The measurement and metrics category focuses on developing reliable auditing mechanisms to distinguish genuine safety improvements from “safety-washing” — a priority that connects to broader AI evaluation efforts documented by the UK AI Safety Institute.

The report explicitly states it is “not advocating for specific policy measures” but rather advancing understanding and developing a research agenda. This restraint is itself noteworthy — the authors recognize that premature policy prescription could foreclose options that may prove essential as AI capabilities evolve. The emphasis on urgency, however, is unequivocal: retrofitting safety measures and coordinating responses after deployment will be far more difficult, and the window for corrective action may diminish or disappear altogether once advanced systems are deployed.

Implications for Organizations and Policymakers

The Carnegie analysis has immediate practical implications for multiple stakeholder groups. For AI developers, the report provides a framework for understanding why voluntary safety investments — while necessary — are insufficient without broader coordination. The free-rider dynamic means that even well-intentioned companies investing heavily in safety operate at a competitive disadvantage unless governance mechanisms level the playing field.

For national policymakers, the analysis offers both an economic justification for public investment in AI safety (filling the market failure gap) and a cautionary tale about the limits of unilateral action. The graduated approach — building national and regional frameworks before attempting global coordination — provides a practical pathway that respects sovereignty concerns while working toward the ultimate goal of global AI safety.

For international institutions, the report suggests that existing governance architecture for climate, health, and nuclear safety provides useful templates but requires significant adaptation. The unique characteristics of AI — particularly the safety-capability paradox and the rapid pace of development — mean that governance mechanisms must be more agile, more technically informed, and more resistant to capture by dominant actors than their predecessors in other domains.

Perhaps most importantly, the report underscores the urgency of action. Unlike climate change, where the physical processes unfold over decades, AI development moves at the pace of software — meaning that the window for establishing effective governance may be measured in years rather than decades. The authors’ call for immediate research, coordination, and institutional development is not merely academic recommendation — it is a warning that delay itself carries significant risk. Organizations seeking to understand these dynamics in depth should engage with the full report, which represents one of the most thoughtful and comprehensive treatments of AI safety governance published to date.