The AI Wager: Four Scenarios for the Future of Work
What Could Happen, What It Means for You, and What to Ask Your Representatives
Introduction
The companies building artificial intelligence are spending more than $660 billion this year on the bet that AI will transform the economy. The economists studying that bet disagree with each other by an order of magnitude about what will actually happen. The workers and communities in the path of the transformation have been given awareness of the disruption but almost no tools for evaluating the range of outcomes — or for holding their elected officials accountable to preparing for each of them.
This brief presents four plausible scenarios for the AI transition, describes what each means for workers and citizens, and provides specific questions that the People’s Council will pose to policymakers under each scenario. No one knows which scenario will unfold. All four require a democratic response that does not yet exist.
Scenario One: AI Delivers — The Transformation Is Real
What happens
AI achieves the productivity gains its proponents project. The investment proves justified. New industries and new categories of work emerge. The economy grows substantially — perhaps by the six to nine percent of GDP that optimistic projections envision. The technology becomes as foundational as electricity or the internet: disruptive in the transition, transformative in the outcome.
What it means for workers and citizens
Even in the best case, the transition is severe for those directly affected. Tens of millions of workers whose jobs are reorganized or eliminated face a period — years, not months — of displacement, retraining, and identity reconstruction. Historical precedent is instructive: electricity was invented in the 1880s but did not produce measurable aggregate productivity gains until the 1920s. The workers displaced during those four decades did not benefit from the eventual transformation. They bore the costs of a transition whose gains accrued to the next generation.
Under this scenario, the economy generates enough new wealth to fund generous transition programs — retraining, income support, portable benefits, community investment. But that wealth does not distribute itself. Every prior technology revolution that increased aggregate prosperity also concentrated that prosperity in the hands of technology owners, unless democratic institutions intervened. The Gilded Age was extraordinarily productive. It was also extraordinarily unequal. The question under Scenario One is not whether AI creates value. It is whether the institutions exist to ensure the value is shared.
What you would experience: If you work in a field directly affected by AI — knowledge work, professional services, creative industries, customer service, financial analysis, software development — you face a period of significant disruption even in the best case. Your job may not disappear, but it will change in ways that require new skills, new professional identities, and new relationships with AI tools. The transition may be manageable if your employer invests in genuine onboarding, if your community provides support infrastructure, and if the policy environment includes funded retraining, income bridges, and portable benefits. Without those supports, even a broadly positive transformation produces concentrated suffering among the people who happen to be in the wrong occupation at the wrong time.
Questions for policymakers
If AI generates the productivity gains its proponents project, what specific mechanism ensures those gains are broadly distributed rather than captured by technology owners and shareholders? The tax code, the labor law framework, and the benefit structure were designed for an economy in which productivity gains flowed primarily through wages. If AI breaks that connection — producing more with fewer workers — what replaces it?
What is your plan for funding transition programs at a scale commensurate with the disruption? The AI Workforce PREPARE Act authorizes six million dollars over five years. The companies driving the transition are spending $660 billion in a single year. What ratio between private investment in displacement and public investment in transition do you consider adequate?
Do you support mandatory reporting on AI-driven workforce changes? Without data on which jobs are being eliminated, which are being created, and which communities are bearing the costs, every other policy response is guesswork. The Warner-Hawley AI-Related Job Impacts Clarity Act would require this reporting. What is your position?
Scenario Two: AI Delivers Partially — Transformative in Some Sectors, Disappointing in Others
What happens
AI produces genuine productivity gains in specific applications — software development, customer service, certain knowledge-work tasks, medical documentation — but falls short of the broad economic transformation that the investment levels assume. Five to thirteen percent of firms achieve significant returns. The rest experiment, pilot, and see modest or no measurable impact. The investment cycle does not crash but corrects: spending moderates, some AI startups fail, the surviving companies consolidate. The technology settles into the economy the way the internet did after the dot-com correction — transformative over two decades, but not overnight, and not evenly.
What it means for workers and citizens
This is, in many ways, the hardest scenario to navigate. The disruption is real but concentrated. Workers in the sectors where AI works well — call centers, routine document production, code generation, data analysis — face genuine displacement. Workers in sectors where AI underperforms — complex healthcare, education, skilled trades, relational professions — face augmentation rather than replacement, but also the anxiety of not knowing whether their sector is next.
The critical challenge under Scenario Two is that the economy has not grown enough to make generous transition programs economically painless. The aggregate gains are real but modest. The political case for large-scale public investment in displaced workers is weaker than under Scenario One, because the transformation feels less dramatic and the benefits are less visible. Yet the workers who are displaced face the same identity disruption, the same community consequences, and the same coherence crisis as under any scenario. Their suffering is not reduced by the fact that it is concentrated rather than universal.
What you would experience: The effects depend heavily on your sector and your employer’s choices. If you work in a sector where AI has proved effective, you may face the same displacement pressures as under Scenario One — but with less public sympathy and fewer resources, because the national conversation has moved on to debating whether AI was “overhyped.” If you work in a sector where AI has disappointed, you may feel relief but also uncertainty: the technology is still improving, the investment is still flowing, and the question of whether your field is next remains open. The most common experience under Scenario Two is not catastrophic job loss but a slow erosion of the tasks that defined your professional identity — a gradual shift from creator to supervisor of AI outputs, accompanied by the cognitive overload and identity disruption that the BCG research on “AI brain fry” has documented.
Questions for policymakers
How will you ensure that workers in AI-disrupted sectors receive adequate transition support even if the overall economic impact of AI is modest? The political temptation under Scenario Two is to treat AI displacement as a normal market adjustment. The evidence from deindustrialization shows that concentrated displacement produces the same human consequences regardless of whether it registers in aggregate economic statistics. What is your commitment to sector-specific transition programs?
What standards will you support for AI implementation in workplaces? The research shows that the design of AI deployment — not AI itself — determines whether workers experience augmentation or replacement, cognitive support or brain fry. Do you support requirements for worker voice in AI implementation decisions, cognitive load limits, and deskilling risk assessment in high-stakes domains?
How will you address the gap between AI hype and AI reality in policymaking? Under Scenario Two, the danger is that policy is designed for a transformation that doesn’t fully materialize, or — more likely — that the absence of a dramatic transformation is used as an excuse for inaction. What evidence will you use to determine the appropriate scale of response, and how will you update that assessment as the technology evolves?
Scenario Three: The AI Bubble Bursts
What happens
The gap between AI infrastructure spending and AI-generated revenue proves unsustainable. A correction occurs — potentially gradual (spending moderates, valuations decline, marginal companies fail) or potentially sharp (a market event triggers rapid repricing of AI assets, cascading through the technology sector and into the broader economy). Historical parallels include the dot-com bust of 2000–2001, when the Nasdaq lost seventy-eight percent of its value, and the telecom bubble of the same era, when ninety-five percent of installed fiber optic cable went unused and the sector lost over two trillion dollars in market value.
What it means for workers and citizens
The critical finding from historical precedent is that displacement does not reverse when the investment thesis fails. After the dot-com crash, Silicon Valley took approximately sixteen years to recover to its prior employment levels. After the 2008 financial crisis, workers who lost full-time jobs experienced the worst re-employment outcomes in a generation: only thirty-five to forty percent regained full-time work within a year, and their earnings remained ten percent lower even a decade later.
Under Scenario Three, three categories of workers are affected simultaneously. First, the workers who were displaced during the AI expansion — whose jobs were automated or restructured during the boom — do not get those jobs back when the bubble bursts. The restructuring was real even if the investment thesis was not. Second, workers in the AI industry itself — the engineers, the data scientists, the infrastructure builders — face layoffs as spending contracts. Third, workers in the communities that oriented their economic strategies around AI infrastructure — the data center construction workers, the support services, the local businesses that served an expanded workforce — face the economic consequences of stranded assets. Data centers employ an average of 1,688 workers during construction but only 157 permanently. Communities built around construction-phase employment face a sharp contraction when building stops.
What you would experience: If you were displaced by AI during the boom and are still seeking re-employment when the correction arrives, you face a labor market that has worsened, not improved. If you work in AI or AI-adjacent industries, you face the same displacement cycle — but without the narrative of inevitable technological progress that justified the disruption. If you live in a community that courted data center development as an economic strategy, you may find that the infrastructure built during the boom has a useful life of three to five years, is poorly suited for alternative uses, and generates far fewer permanent jobs than the construction phase promised. The psychological dimension is also distinct: under Scenario Three, the disruption feels not only painful but pointless. The sacrifice was supposed to be in service of a transformation. If the transformation doesn’t come, the loss of identity and purpose is compounded by the sense that it was for nothing.
Questions for policymakers
What contingency planning exists for an AI investment correction? Deutsche Bank has estimated that AI infrastructure spending contributes roughly 0.8 percentage points of total U.S. GDP growth. If that spending contracts sharply, what is the macroeconomic impact, and what countercyclical mechanisms are in place?
What protections exist for communities that have oriented their economic development around AI infrastructure? Communities in Northern Virginia, central Ohio, and other data center corridors have made long-term commitments — tax incentives, grid infrastructure, land use changes — on the assumption of sustained AI investment. If that investment contracts, who bears the cost? Have those communities been provided with honest assessments of the risks alongside the promised benefits?
What is your position on severance and transition requirements for AI-driven layoffs? Under current law, companies that restructure around AI and then retrench when the investment thesis fails face no obligation to the workers displaced in either direction. Do you support requirements for advance notice, severance, continued benefits, and retraining funding for workers displaced in AI-related restructuring?
How will you address the “double hit” faced by workers displaced during the boom who remain unemployed during the bust? These workers — displaced by AI automation that proved sufficient to eliminate their positions but insufficient to generate the economic growth that was supposed to create new opportunities — represent the population most at risk for the long-term scarring effects documented in prior economic crises.
Scenario Four: The Worst of Both Worlds
What happens
AI proves capable enough to displace workers but not productive enough to generate the economic abundance that would offset that displacement. The MIT Nobel laureate Daron Acemoglu calls this “so-so automation” — technology that improves corporate margins without meaningfully improving overall productivity or creating new categories of employment. The Nobel laureate Joseph Stiglitz described this scenario in March 2026 as a situation in which “we do not have the macro or micro framework for managing that kind of displacement.”
A formal economic model published by researchers at the University of Pennsylvania and Boston University in March 2026 demonstrates why this outcome is not merely possible but structurally incentivized. In competitive markets, each firm captures the full cost savings of replacing workers with AI but bears only a fraction of the demand destruction that displaced workers represent. The result is a Prisoner’s Dilemma: every firm automates because its competitors are automating, and the collective result is that they destroy the consumer base they all depend on. The researchers tested the most commonly proposed solutions — wage adjustments, worker ownership stakes, universal basic income, retraining — and found that none of them eliminates the structural incentive to over-automate.
What it means for workers and citizens
Scenario Four combines the displacement of Scenarios One and Two with the economic weakness of Scenario Three. Workers lose jobs to AI automation, but the economy does not generate enough new activity to reabsorb them. Consumer demand weakens as displaced workers reduce spending. Companies respond to weakening demand by automating further — cutting costs to maintain margins — which displaces more workers, which weakens demand further. The cycle is self-reinforcing.
The historical parallel is the agricultural displacement of the early twentieth century. Mechanization dramatically increased farm productivity, but the displaced farmers had nowhere to go — the industrial economy had not yet grown large enough to absorb them. The result was two decades of rural poverty, mass migration, and social instability that was not resolved until World War II government spending created the demand that the private economy had not. The AI version of this pattern would play out among the professional and knowledge-working classes rather than rural farmers — but the structural logic is identical.
What you would experience: Under Scenario Four, the ground shifts beneath you in ways that feel individually random but are systemically connected. Your company automates a set of tasks and reduces headcount. You search for a new position and find that similar roles across the industry have been restructured. You pursue retraining, but the fields you retrain for are also being automated — the technology is improving faster than the retraining pipeline can adapt. The consumer economy weakens because millions of workers in your situation are spending less. Your community’s tax base contracts. The civic institutions that might have supported your transition — the library, the community college, the mental health services — face budget cuts. You are told that AI is creating new kinds of work, but the new jobs require skills you don’t have, are located in places you don’t live, or pay less than the position you lost. The anxiety is compounded by the sense that no one in a position of authority has a plan adequate to what you are experiencing.
Questions for policymakers
Do you support an automation tax — a Pigouvian tax on AI-driven labor replacement — as a mechanism for aligning the private incentive to automate with the social cost of displacement? The Falk-Tsoukalas research demonstrates that voluntary measures, market corrections, and conventional policy tools cannot eliminate the structural incentive to over-automate. A tax on automation that is calibrated to the social cost of displacement — and whose revenue funds transition programs — is the only mechanism their model identifies as effective. What is your position?
What is your plan for maintaining consumer demand if AI displacement reduces aggregate purchasing power? The consumption paradox — the structural contradiction of an economy that hollows out the purchasing power of its own customer base — is not a theoretical concern. It is the mechanism through which Scenario Four becomes self-reinforcing. What specific fiscal, monetary, or structural policies would you deploy to prevent the demand-destruction spiral?
Do you support the creation of democratic advisory bodies on AI and the workforce that include workers, labor economists, community health practitioners, and civic leaders — not only technology executives and investors? The current Presidential Council of Advisors on Science and Technology includes twelve technology company executives and no labor representatives. Under Scenario Four, the governance of the AI transition cannot be entrusted to the industry driving the disruption. What structural changes to AI governance do you support?
What is your standard for evaluating whether the AI transition is being managed adequately? The People’s Council applies the salutogenic standard: did we restore the conditions under which people can experience their working lives as comprehensible, manageable, and meaningful? That standard measures not only income replacement but identity reconstruction, community restoration, and democratic participation. What standard do you apply, and how will you report publicly on whether it is being met?
What All Four Scenarios Have in Common
Across every scenario, five findings hold:
Displacement does not reverse. Whether AI delivers or disappoints, workers who have been displaced, organizations that have restructured, and communities that have reoriented their economies do not return to their prior state. The restructuring is permanent; only the promised benefits are uncertain.
The existing policy infrastructure is inadequate. No scenario is well served by four pages of federal workforce recommendations, six million dollars in transition funding, or a governance structure composed exclusively of the industry driving the disruption.
The human consequences extend beyond income. Professional identity, community belonging, daily structure, purpose, and the sense that one’s life is comprehensible, manageable, and meaningful — these are disrupted under every scenario, and they are not restored by income replacement alone.
The asymmetry of consequences demands preparation. If we prepare aggressively and the optimists are right, the cost is modest: we will have built civic institutions that strengthen democratic life regardless. If we fail to prepare and the pessimists are right, the cost is measured in human lives and democratic capacity.
Workers and citizens must have a structural voice. Under no scenario is it adequate for the governance of the AI transition to be conducted by the companies driving it, advised by councils they populate, and covered by media platforms they increasingly own. Democratic voice in the governance of the most consequential economic transformation in a generation is not a luxury. It is a precondition.
For Deliberation
The People’s Council invites citizen-delegates to consider the following questions in preparation for the May 22 Conference:
Which scenario do you consider most likely based on what you have observed in your own workplace and community — and does your assessment change the urgency of the response?
If you could ask your congressional representative one question about AI and the workforce, which of the fourteen questions above would you choose — and why?
What institutions in your community — libraries, community colleges, faith communities, civic organizations, professional associations — are currently equipped to support workers navigating AI displacement? What would they need to do so adequately?