Our Approach

In this paper, we introduce an empirical and reproducible approach to monitoring job displacement by TAI. We first classify occupations based on current prompting behavior from a novel dataset from Anthropic, linking 4 million Claude Sonnet 3.7 prompts to tasks from the O*NET occupation taxonomy. We then develop a seasonally-adjusted autoregressive model based on employment flow data from Brazil (CAGED) between 2021 and 2024 to analyze the effects of diverging prompting behavior on employment trends per occupation. We conclude that there is no statistically significant difference in net-job dynamics between the occupations whose tasks feature the highest frequency in prompts and the ones with the lowest frequency, indicating that current AI technology has not initiated job displacement in Brazil.

We investigate the economic potential of Transformative AI, focusing on "temporal coherence"—the ability to maintain goal-directed behavior over time—as a critical, yet underexplored, factor in task automation. We argue that temporal coherence represents a significant bottleneck distinct from computational complexity. Using a Large Language Model to estimate the 'effective time' (a proxy for temporal coherence) needed for humans to complete remote O*NET tasks, the study reveals a non-linear link between AI coherence and automation potential. A key finding is that an 8-hour coherence capability could potentially automate around 80-84\% of the analyzed remote tasks.

This project examines the economic impacts of AI adoption, focusing on its potential to increase productivity while also widening income inequality and regional disparities. It explores the factors influencing adoption rates across industries and concludes with policy recommendations aimed at mitigating these disparities through targeted AI adoption incentives and workforce upskilling programs.

The potential for Transformative AI (TAI) to exacerbate inequality necessitates exploring novel redistributive policies. This document presents a comprehensive framework and reports results from a conceptual simulation study evaluating the economic implications of a Universal Basic Income (UBI) funded by a significant annual tax (e.g., 4%) on company value. Drawing insights from recursive alignment (RA) and functional models of intelligence (FMI), this framework emphasizes policy adaptability and potential failure modes arising from rigid design under TAI's deep uncertainty. Developed for the Economics of TAI Sprint (Tracks 3 & 5), this document details the core research questions, analytical focal points, conceptual model structure, RA-inspired simulation design, and interprets the simulation outputs. It serves as a proof-of-concept and guide for future research into designing resilient economic policies for the TAI era.

The escalating geostrategic importance of frontier AI development increases the likelihood of nationalization. While no explicit plans have emerged in the United States, such action would likely be swift and comprehensive. A government seizure of critical AI infrastructure would fundamentally transform the sector's economic foundation – shifting funding from traditional private sources to the American tax base, thereby repositioning AI as a public good. The objectives driving development would similarly pivot from user engagement to national security imperatives. Given the history of American adversaries pursuing intellectual property theft, this transition would likely establish a more restrictive diffusion model that prioritizes security over openness. By tightly controlling crucial elements of the AI stack, that approach risks diminishing the broader societal benefits that might otherwise emerge from AI advancement.

We studied AI’s impact on India’s IT sector. We modelled a 20% labour shock and proposed upskilling and insurance policies to reduce AI-driven job losses.

The economic transformations induced by artificial intelligence (AI) raise pressing distributional concerns. This paper examines the allocation of the AI Dividend—the surplus value generated through AI advancements—and proposes mechanisms to ensure equitable redistribution. Anchoring our analysis in the Distribution track, we focus on the Data as Labor (DaL) framework, inspired by Lanier and Weyl, wherein individuals' data contributions are treated as productive labor. We simulate and compare two paradigms: Data as Capital (DaC), in which data is aggregated as corporate capital, and DaL, wherein individuals are compensated for their data. Using comparative economic simulations, we highlight systemic inequalities emerging under DaC and demonstrate the stabilizing potential of DaL structures. We further subdivide the DaL paradigm into two mechanisms: a corporate taxation regime and an individualized data compensation model, proposing a novel formula for micro-level redistribution. The implications of these models for labor markets, inequality, and societal stability are discussed, with a focus on designing incentive-compatible and scalable economic policies. Our findings suggest that recognizing data as labor not only promotes distributive justice but also enhances the long-term sustainability of the AI-driven economy.Message @vi

This paper analyzes five interlinked fiscal measures proposed to fund a Universal High Income (UHI) system in response to large-scale technological automation: a unity wealth tax, an unused land and property tax, progressive income tax reform, and the Artificial Intelligence Dividend Income (AIDI) program. Using dynamic general equilibrium modelling, IS-MP-PC frameworks, and empirical elasticity estimates, we assess the macroeconomic impacts, revenue potential, and distributional consequences of each measure. Results indicate that the combined measures could generate 8–12% of GDP in annual revenue, sufficient to sustainably support a UHI framework even with 80–90% unemployment. The wealth tax and land tax enhance fiscal resilience while reducing inequality; the progressive income tax improves administrative efficiency and boosts aggregate consumption; the AIDI channels the productivity gains of automation directly back to displaced workers and the broader public. Nonetheless, each policy presents limitations, including vulnerability to capital flight, political resistance, behavioural tax avoidance, innovation slowdowns, and enforcement complexity. AIDI, in particular, offers a novel mechanism to maintain consumer demand while moderating excessive automation, but demands careful regulatory oversight. Overall, the findings suggest that, if implemented carefully and globally coordinated, these measures provide a robust fiscal architecture to ensure equitable prosperity in a post-labour economy dominated by artificial intelligence. Strategic design and adaptive governance will be essential to maximize economic stability, technological innovation, and social welfare during this unprecedented economic transition.

This project examines how Transformative AI (TAI) could reshape economic growth in African labor markets, potentially deepening inequality if its benefits are not equitably distributed. As AI automates processes and shifts workforce dynamics, understanding its AI distribution effects is crucial to preventing disproportionate gains among a few while leaving others behind. The study employs macroeconomic modeling to assess market dynamics, tracing AI’s impact on labor and capital concentration. Additionally, case studies of past technological disruptions provide insights into successful policy interventions that mitigated inequality. Stakeholder surveys with African policymakers, entrepreneurs, and workers help contextualize AI’s economic influence and identify pathways for equitable adaptation. Expected outcomes include a predictive model for AI-driven inequality trends, a policy toolkit supporting reskilling and localized AI adoption, and an open-access dataset capturing AI’s labor market effects in LMICs.