Mapping Tax Risks From Labour-Displacing AI

AI Safety
Written By Future Impact

← Back to the Research Library

FIG Fellows: Trish Ieong, Akbar Saputra, Anuja Maniar

Authors: Trish Ieong, Akbar Saputra, Anuja Maniar, Deric Cheng

Abstract:

This paper addresses an emerging fiscal risk for tax policymakers: the potential for powerful, productivity-enhancing AI systems to erode tax bases through labour displacement. We identify four channels through which fiscal pressures may arise, simulate scenarios for an ‘average OECD country’ under various assumptions, and examine country-specific factors likely to produce different results across jurisdictions. Our aim is to prompt governments to start preparing now, and we offer some high-level suggestions for doing so.

View publication
Future Impact
Previous

How's it going? Reinforcement learning in language models recruits a functional welfare axis
Next

Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training