AI can radically disrupt working lives and material well-being for better or worse — supporting the discovery of new medicines or raising farmer yields while concentrating wealth and widening inequality between countries.
Which future we end up with, and for whom, is not fixed. It will be determined by the choices civil society, labor, industry, and policymakers globally make in the next few years.
That uncertainty is visible in the data coming out right now. For example, U.S. employers have cited AI as the leading reason for job cuts every month this year, tying more than 100,000 layoffs directly to AI through June, nearly double the total for all of 2025. But it’s contested whether this reflects a genuine shift in what AI can do to knowledge work, or “AI-washing,” where companies use AI as cover for a correction from pandemic-era overhiring.
Why Scenario Planning
PAI’s Guidelines for AI and Shared Prosperity, released in 2023, were used as a north star for the U.S. Department of Labor’s 2024 principles to advance equity and job quality. But since then, uncertainty about the future path of the economy has continued to grow. This is because AI’s potential impact on the labor market depends on the pace of technical progress in areas like post-deployment learning and robotics, how quickly industries adopt and see returns, and the policy environment that shapes both.
Given the high uncertainty about AI’s technological progress and adoption, scenario planning is a powerful tool for strategic planning and preparation. But given the multitude of factors that could affect AI’s trajectory, effective scenario planning requires diverse expertise, bringing those who understand AI’s technical trajectory together with those who understand how worker power can shape technology.
To address this need, earlier this year, PAI launched a new initiative – Shaping Economic Futures in the AI Era and convened a multi-stakeholder Labor and Economy Steering Committee to guide the effort. This work is designed to leverage scenario planning as a tool to help society shape what future we want. Scenarios are tools for pressure-testing assumptions and surfacing the choices that matter most, giving participants a shared, concrete future to deliberate on rather than an abstract debate about probabilities.
Workshopping Two Scenarios: Slow and Fast
As part of this effort, on July 29–30, we’re co-convening a scenario planning workshop in Washington, D.C. with the Windfall Trust, bringing together leaders from labor unions, industry, and civil society alongside our Labor and Economy Steering Committee. Participants will spend the workshop inhabiting two stylized 2030 scenarios, and then work backward to what we’d need to start doing in 2026 to steer toward better outcomes under either one. These two scenarios draw in part on the OECD’s February 2026 report modeling a range of AI capability trajectories through 2030:
- In the ‘Slow’ scenario, AI keeps improving but at a decelerating pace, remaining concentrated in knowledge work. Underemployment, contract work, and income volatility spread beneath the surface, without the headline unemployment numbers that would otherwise prompt a policy response.
- In the ‘Fast’ scenario, AI capability grows faster than the current rate alongside advances in robotics. Job losses outpace new job creation and automation gains concentrate rapidly among asset owners, arriving far faster than tax, safety-net or fiscal systems built for gradual change can adapt.
The workshop is held under the Chatham House Rule so participants can speak candidly about where they see risk and opportunity. Insights from the convening will be used to update PAI’s Guidelines for AI and Shared Prosperity and develop Recommendations robust to multiple scenarios.
What Comes Next
Scenarios are a tool and a starting point, not the destination. Alongside them, we’re bringing a starter list of draft recommendations spanning areas like international coordination, public investment, taxation and market structure, and worker power—building on our 2023 Shared Prosperity Guidelines and initial discussions with our Labor and Economy Steering Committee this spring. We’re asking participants to pressure-test that list, flag what’s missing, and help us identify the “no-regrets” actions worth taking regardless of which future could unfold. Workshop discussions will help shape a detailed report this Fall about the Scenarios by the Windfall Trust developed in partnership with PAI, and inform Recommendations that PAI will develop by year-end leveraging this scenario analysis. We don’t think the uncertainty over AI’s pace is a reason to wait for clarity that may not arrive in time. It’s the reason to plan for more than one future at once, and to start building the actions now that would hold up under either.
Facts Only
PAI released Guidelines for AI and Shared Prosperity in 2023.
The U.S. Department of Labor used these guidelines for its 2024 principles on equity and job quality.
U.S. employers cited AI as the leading reason for job cuts every month in 2024.
Over 100,000 layoffs were tied directly to AI through June 2024.
PAI launched the Shaping Economic Futures in the AI Era initiative.
A multi-stakeholder Labor and Economy Steering Committee was convened to guide this initiative.
A scenario planning workshop was co-convened with the Windfall Trust on July 29–30 in Washington, D.C.
Participants included leaders from labor unions, industry, and civil society.
The workshop operated under the Chatham House Rule.
Two scenarios for 2030 were used: "Slow" (decelerating AI progress concentrated in knowledge work) and "Fast" (rapid AI and robotics growth outpacing job creation).
These scenarios draw on an OECD report from February 2026.
A detailed report on scenarios by the Windfall Trust is scheduled for release in the Fall.
PAI intends to develop recommendations by year-end.
Executive Summary
AI's impact on global labor and wealth distribution remains highly uncertain, with potential outcomes ranging from breakthroughs in medicine and agriculture to increased inequality. Current data shows significant job losses in the U.S., though there is an active debate over whether these are caused by genuine technological displacement or "AI-washing" to mask pandemic-era overhiring.
To navigate this volatility, a multi-stakeholder effort involving PAI, the Windfall Trust, and various labor and industry leaders is employing scenario planning. By analyzing two distinct trajectories—a "Slow" scenario characterized by decelerating growth and hidden underemployment, and a "Fast" scenario where rapid automation overwhelms existing fiscal and safety-net systems—planners aim to identify "no-regrets" actions. The goal is to develop policy recommendations regarding taxation, public investment, and worker power that remain robust regardless of the actual pace of technical progress.
Full Take
The strongest version of this narrative is that the window for proactive policy is closing, and since we cannot predict the exact speed of AI's evolution, the only rational strategy is to prepare for multiple divergent futures simultaneously. By shifting the conversation from "probability" to "possibility," this approach attempts to bypass the paralysis of forecasting.
The underlying paradigm is one of strategic institutionalism: the belief that the future is a product of deliberate choices made by a coalition of "civil society, labor, industry, and policymakers." However, there is an unstated assumption that these groups have the political will and capacity to coordinate effectively before the "Fast" scenario's tipping point is reached. The narrative echoes historical industrial transitions, but with a critical difference in velocity; the fear is that the gap between technological disruption and policy adaptation will be too wide to bridge.
The implications for human agency are significant. If the "Fast" scenario manifests, the cost is borne by the labor force while the benefits concentrate among asset owners, potentially rendering current social safety nets obsolete. The push for "no-regrets" actions suggests a move toward systemic resilience over specific predictions.
Patterns detected: none
The root cause of this framing is the tension between exponential technological growth and linear institutional change. It positions strategic planning as the only hedge against systemic instability.
Bridge Questions:
If "AI-washing" is prevalent, how does that distort the data used to build these scenarios?
Which stakeholders possess the most leverage to actually steer the outcome, and are they represented in the current steering committee?
What specific "no-regrets" actions could possibly be robust enough to serve both a slow-drift and a fast-collapse economy?
Counterstrike Scan: A bad actor pushing this narrative would use it to create a sense of inevitable crisis to justify the centralization of economic planning or the implementation of restrictive "safety" regulations that actually protect incumbents. The current content does not match this pattern; it emphasizes multi-stakeholder transparency and "pressure-testing" assumptions rather than prescribing a singular, closed-door solution.