The Missing Institution: A Global Dividend System for the Age of AI

A Global Economic Transition We Are Not Ready For

Over the past decade, the conversation around artificial intelligence has been dominated by a familiar set of concerns: breakthroughs in machine learning, competitive pressures between labs, and how to govern rapidly advancing systems that even their creators don’t fully understand. Yet far less attention has been paid to a more profound, systemic question: How will AI transform the economic foundations of society?

No one can predict the future with certainty. But one scenario that deserves far more attention is the possibility that advanced AI could dramatically reduce the role of human labor in the economy. What happens if machines can do an increasing portion of economically valuable work better, faster, and cheaper than people? How would we distribute wealth, maintain social cohesion, and preserve human dignity in a world where labor is no longer the organizing principle of economic life?

We are approaching a world where advanced AI functions less like a tool and more like a drop-in replacement for a white-collar worker—a shift that is no longer theoretical, but that researchers at leading AI labs have come to expect in the next few years. This is a more profound disruption than most current economic policy debates are accounting for—but that’s precisely why it requires our attention now.

Importantly, this isn’t some fringe worry. It’s a design goal, embedded in the mission statements of leading AI labs. OpenAI’s charter, for instance, explicitly commits to building systems that “outperform humans at most economically valuable work.” Whether you believe that’s just over the horizon or still decades away, the pursuit of this future is already underway and the fact that it’s being seriously pursued and funded should give us pause. This raises urgent questions about what happens if the labs succeed in their stated goal. Questions that should be addressed before such capabilities are realized.

The countries most exposed to AI-driven disruption are often the least equipped to shape how its benefits and burdens are shared. They lack the tax base to cushion job loss, the institutional infrastructure to adapt, and the geopolitical leverage to demand inclusion. Left unaddressed, this asymmetry could widen existing global inequalities and destabilize already fragile political orders.

Most policy conversations today focus on national fixes: tax reform, regional UBI pilots, retraining programs. But what do we do when the problem is global?

Defining the World We’re Planning For

Too often, debates about AI and the economy break down because of unspoken differences in assumptions—about dominant risks, timelines, or where the disruption will hit.

Some imagine a slow wave of job displacement offset by eventual job creation. Others see a future where human labor is largely obsolete. Still others anticipate a world where AI supercharges productivity but the gains flow disproportionately to the most talented and industrious, while everyone else treads water. These futures demand different responses and without clarity about which one we’re planning for, it can be hard to have a coherent conversation about policy, let alone priorities.

So let’s be specific about the scenario this proposal engages with: it’s a world where advanced AI systems can perform a growing share of economically valuable tasks. Not just drafting emails or debugging code but coordinating logistics, optimizing business strategy, designing drugs, producing media, and autonomously completing complex, multi-week projects—more efficiently and cost-effectively than humans. In this world, labor’s role in value creation shrinks significantly and wages begin to decouple from productivity. The share of income going to workers declines while value accrues with capital owners. Millions find themselves displaced from jobs.

In this world, wage-based incomes could stagnate or fall, even as goods and services become cheaper to produce. If purchasing power falls faster than prices, cheaper goods won’t translate to greater well-being. And so we may find ourselves in a paradox: high productivity, low demand. What happens to supply chains, investment, and economic dynamism when consumer demand falters, not from scarcity, but from exclusion?

At the same time, wealth will begin to concentrate among the firms best positioned to automate labor at scale—AI labs, cloud providers, chipmakers, and large corporate users, all of whom stand to capture enormous value. Not everyone can become an entrepreneur or license proprietary models. An early glimpse into pricing models suggests that access to the most capable systems will remain at the level of firms and out of reach for most individuals. The result may be a bifurcated economy, where a minority of firms operate at superhuman efficiency, while the majority struggle with weakened demand and eroding margins.

Meanwhile, governments could face a growing fiscal squeeze. Today’s tax systems rely heavily on income taxes from workers, while often failing to capture corporate profits effectively due to tax loopholes, profit shifting, and relatively low rates on business income. As AI reduces the role of human labor and allows companies to operate with fewer employees, this setup begins to break down. With less income to tax from workers, government revenues decline—just as the demand for public support rises. In many countries, social safety nets are already fragile. Without reform, the gap between what governments can provide and what people need is likely to widen.

This future is also geopolitically asymmetric. Countries home to leading AI labs and digital infrastructure may capture a disproportionate share of the economic value. Others, especially those reliant on cheap human labor or foreign remittances would struggle to adapt.

For decades, offshoring to low-wage countries has been a rational strategy: it maximized margins and minimized labor costs. But in a world where robotics and automation continue to advance, that logic could begin to reverse. If machines can undercut even the lowest wage floors, it might become more cost-effective for companies to bring production back home. That shift wouldn’t just simplify logistics; it could undermine the foundations of economic growth in many parts of the Global South, where export-driven industrial jobs have long been central to national development. If those jobs were to disappear without new ones rising in their place, the social and economic consequences could be profound.

This isn’t just a theoretical concern. Many governments in the Global South already face strained budgets and limited capacity to deliver large-scale social support. And yet the economic asymmetry is likely to deepen—even by most conservative accounts. PwC estimates that AI could add $15.7 trillion to global GDP by 2030, but just $1.7 trillion of that is projected to reach the Global South (excluding China). The divide is not just about money but about the tools for resilience, fiscal capacity and institutional readiness.

This scenario shouldn't be treated as a forecast, and it certainly isn’t the only future we should prepare for. A more decentralized world, where access to AGI is widespread and development is diffuse, would bring its own economic dynamics and governance challenges. The goal isn’t to bet on one outcome, but to map the range of plausible trajectories where our current institutions fall short.

But across many trajectories, one thing is consistent: we’re entering a world our existing institutions weren’t designed to navigate.

Beyond Borders: Distributing Value Through a Global Dividend

If this is the future we’re heading toward—a world of soaring output but declining roles for human labor—then the question isn’t just what will happen, but what kind of institution could meet that challenge. We’ll need mechanisms that don’t just stabilize economies, but match the global scale of the disruption. One emerging proposal is a system to distribute AI-generated value across borders, anchored in a clear but ambitious idea: a global dividend.

A global dividend system would deliver recurring payouts to individuals, grounded in the principle that every person holds a legitimate claim to a share of the value created by transformative technologies. This isn’t about charity, it’s about recognizing that modern AI systems are built atop shared infrastructure, public data, and a long arc of collective human knowledge. If AI labs want broad exemptions from copyright law, while the rest of society wants a living income, then the political question becomes: why not exchange one for the other?

The institutional form this might take is still experimental, but one early vision resembles a global sovereign wealth fund: a vehicle to hold and grow AI-driven economic surplus, not in the name of any one country, but in service of humanity as a whole. And while the global scale is unprecedented, the core idea is not.

Alaska’s Permanent Fund Dividend, for example, distributes annual cash payouts—typically between $1,000 and $2,000—to every resident, funded by the state’s oil revenues. These payments are statutory, not contractual: Alaskans do not hold formal ownership, and the state can reduce or pause distributions during budget crises.

By contrast, the Eastern Band of Cherokee Indians distributes income dividends—ranging from $3,000 to $6,000 twice per year—from a sovereign wealth fund built on casino revenues. These dividends are protected under tribal law, with community-led governance and legal entitlements. Children’s shares are placed in trust and accessed at adulthood, often with financial literacy training required. It’s a powerful model of how sovereign wealth can be shared directly, equitably, and intergenerationally.

A global dividend would build on that logic, extending it beyond borders and anchoring it not in geography, but in our shared exposure to the economic upheaval AI may bring. Such a system could encode economic stakeholding where individuals hold formal, durable claims to wealth derived from an AI-driven economy.

This isn’t completely outside the Overton window—even some industry leaders have gestured towards exploring ownership-based models. OpenAI CEO Sam Altman, for instance, proposed in Moore’s Law for Everything (2021) that large corporations be made to provide equity stakes to a public fund, which could make annual distributions to all US citizens. His proposal was national in scope, but the underlying intuition applies more broadly: that alignment and inclusion may depend not just on access, but on ownership.

In practice, that system could start small. A rising global income floor beginning with the poorest communities and expanding over time. This offers one pathway to deploy funds in the near term and help people even before enough wealth for full global coverage is generated. It’s a way to begin building the infrastructure and legitimacy needed for broader distribution later, while offering immediate support where it’s needed most.

In the long run, such a system could ensure that everyone retains the means to meet basic needs in a world where labor is no longer the main vehicle for income. But even in the short term, modest distributions could serve as a stabilizing force and targeting the most exposed could soften the landing and buy time for broader adaptation.

One of the key challenges will be legal: how do we codify the idea that every person has a rightful economic stake in this emerging infrastructure? That will require creative legal thinking to embed personhood-based entitlements into a durable and enforceable institutional framework. This reframes economic inclusion as a fundamental human right. A form of economic membership in a shared global system.

History offers lessons in how wealth-sharing mechanisms can succeed—or fail—depending on design. After the fall of the Soviet Union, Russia launched a bold experiment in voucher privatization: distributing shares of state-owned companies to citizens to jump-start a market economy. But with little public education, most people, unfamiliar with markets and in dire need for cash, sold their vouchers cheaply to brokers. The result was a dramatic concentration of wealth and the rise of the Russian oligarchy. A year later, Czechoslovakia adopted a similar approach but achieved better outcomes. Why? The Czech government paired privatization with extensive campaigns to educate citizens while the country had a more stable economic context at the time of privatization.

The lesson is clear: redistribution mechanisms need to be trusted, understood, and protected before value is captured.

Building something like this wouldn’t be easy. It would require legal imagination, international coordination, and public trust. It would likely face political resistance from those reluctant to cede sovereignty, and from industries wary of redistribution. But the idea rests on a principle that deserves more space in our collective imagination: collective stewardship of shared wealth in an era of massive disruption.