Economic policy for AGI

Growing anxiety about how AI will impact jobs and the economy is emerging as a major issue across broad swaths of society. It is already a top concern for political candidates and for younger generations – all before the effect of AI shows up in most economic data.AI capabilities have increased tremendously over the last few years, and they show no signs of slowing down. A future economy infused with artificial general intelligence (AGI)—advanced AI systems capable of performing cognitive tasks at least at the level of the average person—could bring huge benefits. This includes improvements in health, leisure time, and higher standards of living. Yet concerns about AI’s economic impacts make sense. In the past, major technological transformations have boosted long-run living standards, granting humanity prosperity and health that would have been unfathomable to our ancestors. Yet in the short term, significant transformations have often also produced lower wages for many, hollowed out regional economies, and deepened inequality.In other words, many people who lived through technological transitions were hurt by the periods of innovation that they lived through. The question is: Can we reduce or avoid such harm this time, while preserving the benefits of technological progress?In the past, countries have struggled to adequately manage productivity-boosting economic transitions. During the Industrial Revolution and the early 20th century, policy often came after the fact, deploying redistributive interventions once the shock and disruption had occurred. In some instances, this may have been due to poor institutions and access to data. In others, slower responses were the result of concerns about acting too early in creating technology-adjustment policy.Previous shocks have also differed from AGI in important ways. Unlike prior technologies that automated routine work and created new industries, by automating intelligence itself, AGI could allow for far more comprehensive automation of cognitive work. This would risk outpacing society’s ability to adapt to the changes.Yet precisely because AGI automates components of ‘intelligence’ itself, the economic impacts of widespread diffusion are very uncertain. At one extreme, AGI’s economic gains and disruption could fail to materialize as expected. Alternatively, it could more closely resemble familiar—though still very significant—prior technology shocks, which disrupted some jobs but eventually created others. Indeed, since the Industrial Revolution, labor has remained a relatively constant force in the economy–contrary to consistent predictions of mass labor displacement—and economists are currently not seeing definitive evidence of systemic employment or wage impacts. At the other extreme, however, the broad and dexterous nature of AGI capabilities could mark a break with prior technologies, reducing demand for human labor in most cognitive occupations.Amid this uncertainty, there are at least a few reasons for hope that society can better manage a disruptive technological shock this time around. In the past, policymakers were limited by weaker and less knowledgeable institutions than we have today. Social science has made strides in understanding how technological shocks unfold and how to measure the impact of new innovations on labor markets. We also know more about the toolkit governments have to implement policies and how to scale them quickly.Since we may not be able to preemptively determine which AGI economic scenario will materialize —or its timescale—it is important to develop flexible economic policy responses tied to clear empirical triggers. This approach can help us avoid acting too late to cushion real disruption or overreacting prematurely with interventions that produce a cure worse than the disease. To develop these sorts of policies, we need to overcome three challenges.The first is our lack of granular and timely labor-market data. Platform usage metrics cannot tell us how AI alters net employment, workplace field experiments rarely generalize to the macroeconomy, and official administrative datasets may lag real-world disruption. Better data on both current statistics and new measures—such as on consumer demand for AI and bottlenecks to adoption—may help us understand how governments ought to respond to the potential advent of AGI. The second challenge is determining which policies might actually distribute the benefits of AGI widely while ensuring that people thrive and have agency over their lives. And the third challenge is that there isn’t currently a common rubric to assess the strength of one potential policy intervention versus another, across a set of standardized dimensions that society cares about. This includes clear data-based indications of when to deploy any particular policy response.To help address this gap, we introduced a unified framework for evaluating potential policies across four dimensions:Welfare and Resilience: A policy’s impact on material living standards, meaning and purpose, and overall economic stability.Agency and Voice: A policy’s impact on individual economic choice, direct ownership of AI-driven gains, and democratic participation.Feasibility and Efficiency: A policy’s political support, popular approval, economic cost, administrative simplicity, speed of rollout and institutional readiness. This includes whether policies create the conditions for AGI-enabled economic growth.Durability across potential AGI economic futures: A policy’s suitability across scenarios marked by mild disruption, broad displacement and complete economic transformation.With this rubric in mind, we used a combination of manual literature reviews, surveys, and 51 AI agent raters trained on real economists to evaluate 11 commonly discussed policy ideas for managing the potential AGI economic transition. For the agent-deliberation analysis, each AI agent’s personality was designed using survey data from 51 real economists, capturing a diversity of economic and political attitudes. This method, pioneered by John Horton, removes some degrees of researcher freedom, allowing us to test how agents ‘rate’ particular interventions based on the available evidence and their own deliberative process.The output of this methodology suggests that no single initiative is the answer to everything. Some policies enjoy popular support but lack practical feasibility. Others appear resilient to AGI disruption but could undermine human agency. Since each intervention carries distinct trade-offs between cost, administrative feasibility, and human agency, an effective strategy should be sequenced around observable thresholds where possible. This strategy may also require investing in infrastructure to track economic data and measure it against predetermined thresholds that can help society determine when to introduce already-designed policies.Rather than endorsing a single static prescription, our methodology identifies three "least-regret" interventions matched to distinct scenarios:Preemptive Stabilizers for Mild Scenarios (Scenario 1): Expanded Unemployment Insurance (UI), Earned Income Tax Credit (EITC) and employer-led retraining - low-regret interventions that connect benefits to work-attachment, and automatically adjust in scale if disruption increases.For Moderate Displacement & Wage Compression (Scenario 2): Transformation of EITC into Negative Income Tax (NIT)—more pronounced interventions that could be triggered if data reveals prolonged unemployment spells and falling median wages that outpace job reinstatement.For Structural Labor-Capital Decoupling (Scenario 3): A Universal Basic Capital (UBC) backstop—more significant interventions that could be designed now but deployed if macroeconomic indicators indicate a sustained decline in labor’s share of GDP alongside increasing capital returns.We assessed each of the 11 policies across measures of welfare, agency, feasibility and efficiency, and durability. The full analysis, methodology and evaluation of 14 potential funding mechanisms for each policy are described in our Economic Policy for AGI paper.11 policies we evaluatedTaxonomy and operational scope of the evaluated household-facing interventions Policy InterventionDefinition & Operational ScopePANEL A: TARGETED LABOR-MARKET & WAGE INTERVENTIONSActive Labour Market Policies (ALMPs) & RetrainingState-sponsored programmes designed to reskill displaced workers and realign their human capital with AI-complementary occupations.Wage InsuranceA transitional subsidy that compensates displaced workers for a portion of the income lost when they secure re-employment at a lower nominal wage.Earned Income Tax Credit (EITC)A means-tested, refundable tax credit functioning as a wage subsidy for low-to-moderate-income workers.Federal Jobs GuaranteeThe government becomes the employer of last resort, ensuring that anyone willing to work is provided with a publicly funded position.Unemployment InsuranceA macroeconomic stabiliser providing temporary, partial wage replacement to displaced workers.PANEL B: UNIVERSAL FLOORS, SERVICES, & STRUCTURAL ASSETSNegative Income Tax (NIT)Establishes a guaranteed minimum income floor, automatically providing cash transfers to individuals whose income falls below a threshold.Universal Basic Income (UBI)An unconditional cash transfer delivered uniformly to all citizens regardless of their employment status or wealth.Sovereign AI DividendA universal and unconditional cash payout funded by the taxation or public ownership of foundational artificial intelligence assets.Universal Basic Capital (UBC)The state-facilitated provision of an ex-ante asset or equity endowment (e.g., trust accounts or shares in a sovereign wealth fund).Universal Basic Services (UBS)The direct and unconditional public provision of essential services, such as healthcare, housing, and transport.Industrial PolicyStrategic state intervention and targeted capital