Welcome to the Predistribution AI Lab

For too long, our economy has short-changed the workers, communities, and other stakeholders that are critical to the value creation process. But the advent of AI is an inflection point. Neither capitalism nor democracy can function with vast imbalances in wealth and power.

The Predistribution AI Lab is facilitating peer-learning and co-creative opportunities for companies, investors, their stakeholders, and other specialists to gather and refine solutions. The Lab will be guided by  the following principles:

Ownership

Compensate workers in companies and communities in infrastructure and natural resource projects for the risks that they take and the value that they create in the production process with equity-linked compensation.

Why Broaden Ownership

Broadening equity ownership of companies supports broad-based prosperity and aligns the incentives of companies and their stakeholders. This contributes to stronger corporate performance, even as the number of owners increases. This expansion also restores trust in institutions.

What This Might Look Like in Practice

Workers receive equity-linked compensation in addition to a living wage, proportional to their salaries and modeled after executive compensation.

  • Companies map other core stakeholders who take risk and create value, such as communities hosting data centers, power plants, and natural resource projects, and compensate them with equity, thereby aligning incentives and contributing to broad-based prosperity.
  • AI companies offer equity-linked compensation to content creators and other professionals whose knowledge is being used to train technology.
  • Diversified investment accounts complement the above to avoid risks of equity exposure to just one company.

‍

Governance

Center the valuable perspectives of corporate stakeholders, including workers, communities, and consumers, through corporate governance reforms.

Why Broaden Corporate Governance Participation

Workers, communities, and consumers have a strong sense of what is happening “on the ground” with companies. They have valuable insights that can help companies stay ahead of the curve on emerging opportunities and risks.

When it comes to training AI and AI safety, incorporating a variety of perspectives will ensure this incredible technology works broadly for humanity. Breaking through the glass ceiling to offer opportunities for stakeholders to participate in corporate governance, including in the boardroom , can also help restore trust in institutions.

What This Might Look Like in Practice

Workers, community members, and consumers can participate in corporate governance training and be considered for roles in companies that enhance corporate governance, such as works councils, advisory councils, or board directorships.

‍

Balance Versus Concentration

Maintain a decentralized approach to ensure that workers and communities do not become dependent on meager benefits from universal basic income or distributions from sovereign wealth funds alone.

Why Take a Decentralized Approach

If society relies on universal basic income or a sovereign wealth fund alone, workers and community members who take risks and create value will not be rewarded directly and commensurately for their contributions to the production process.

Productivity and societal decision-making, therefore, will remain concentrated in the hands of the relatively few owners who control the bulk of productive assets. Such concentration threatens the dynamism of markets and the health of functioning democracies.

What This Might Look Like in Practice

Companies reform their contracts with stakeholders to include them in ownership and governance, thereby fostering agency, dignity, and respect rather than leaving most of the society dependent.

‍

Holistic and Co-Creative Approach

Support adjacent, necessary solutions and iterate proposals with others.

Why Take a Holistic and Co-Creative Approach

The economy is a complex system. There is no “silver bullet” solution.

Different stakeholders, companies, investors, and geographies will have different needs and priorities. A mosaic of ideas can help identify blind spots and avoid unintended negative consequences.

What This Could Look Like in Practice

Integrate governance and ownership solutions with other approaches, such as training and reskilling programs that advance opportunities for workers and small businesses through economic transitions.

Bring together companies, investors, their stakeholders, and specialists in collaborative design workshops.

The Predistribution AI Lab is launching with three foundational discussion papers, which include macro-financial stress tests demonstrating the incentive for change. The papers elaborate on the above proposed solutions, to be refined and workshopped further. We hope you will join us in the co-creation process!

Read the Papers:

This discussion paper, the first in PDI's Predistribution AI Lab series, analyzes four scenarios, modeling the cascading effects of income erosion and unemployment on consumption, tax revenue, mortgage markets, corporate debt, equity values, pension systems, and insurance assets. Scenarios are designed to illuminate key transmission channels of potential macro-financial risk through the real economy, markets, and to diversified investment portfolios. Three scenarios are based on higher unemployment numbers as predicted by Anthropic CEO, Dario Amodei, while the “lighter” scenario is built on the historical precedent of declining returns to labor and a “fissured workplace” even as employment has grown with technology. We do not take a view on whether AI will lead to higher unemployment. Rather, we center our attention on the risks of historical and ongoing declining returns to labor that are shared across stakeholders in society, including financial risks to diversified investors’ portfolios. We argue that the advent of AI is an inflection point at which the world is either poised to deepen the current trends toward risk, or at which we can sculpt and refine economic structures to avoid such risks. This first paper primarily focuses on macro-financial analysis. However, safety, blind spots, and cognitive bias risks are also considered, particularly in Part II of the discussion paper series.
Read More
This discussion paper, the second in PDI's Predistribution AI Lab series, offers practical models for addressing risks identified in Part I of the series, particularly via broadening equity-linked compensation and corporate governance participation to include workers, communities, and content creators who take risk and create value alongside executives and investors in the production process. The report further contextualizes the current technological transition in the broader backdrop of decades of declining returns to labor versus capital, depreciating cash relative to appreciating asset values, and corporate governance that is more strongly oriented toward shareholders versus other corporate stakeholders. At the core of Part II is a recognition that the economy and productivity have been advanced in recent history through the contributions of workers (formally and informally employed, collectively “human capital”), communities who host infrastructure and natural resources projects (collectively “social capital”), and content creators and others who provide data which has advanced technology (a mix of human and social capital). However, with financial capital being prioritized by corporate governance over human, social, and natural capital, these other stakeholders have not been compensated in a manner that keeps pace with financial capital, resulting in rising economic inequality, misalignment of incentives across stakeholder groups, disenfranchisement, loss of trust in institutions, polarization, and domestic and geopolitical conflict. Broadening ownership and governance of companies can align stakeholder incentives to contribute to technological advancements, leverage important perspectives to safely train and roll-out AI, and sustain the aggregate demand upon which the economy and financial portfolios depend.
Read More
This discussion paper, the third in PDI's Predistribution AI Lab series offers a specific prototype of an intervention designed for rideshare drivers in the context of autonomous vehicles (AVs). A three-pillar structure is proposed to offer ongoing cash incomes, equity participation in AV platforms, and diversified investment accounts to displaced and working drivers. The model offers foundational proposals to be further refined with stakeholders and can be adapted to other contexts, including communities hosting infrastructure and natural resource projects and content creators whose intellectual, creative, and personal capital is being used to train AI. We highlight the importance of living wages (incomes) for those who continue to work, as well as freedom of association and collective bargaining. We also compare the proposals we offer to those already on the table, from universal basic income (UBI), to sovereign wealth funds (SWFs), to purpose trusts and beyond, and evaluate pros and cons of various approaches.
Read More

In the coming weeks, we will be breaking down these ideas into engaging content and inviting stakeholders to co-create with us. To stay informed and engage, please follow us on LinkedIn and X and subscribe to our newsletter.