Contribute to a modeling project comparing the economic value of restricted agentic AI systems vs. fully general ones, to assess whether "turning the dial down" can preserve most benefits while substantially reducing loss-of-control risk.
About the project
An implicit assumption in AI policy is the trade-off between restricting agency/generality to reduce risk and increasing economic value (see https://www.rand.org/pubs/research\_reports/RRA4220-1.html). If a lot of value can be extracted from AI systems with restricted agency, then restrictions are cheap; if producing the most potential value requires full autonomy and generality, then restrictions are costly.
The lab is initiating a project along these lines: (i) defining operational levels of autonomous agency; (ii) mapping economic value across these levels using AI economic automation models; (iii) combining this with a risk model to produce risk/benefit frontiers.
Theory of change
If enough economic value survives meaningful agency restrictions, this changes the political feasibility of frontier AI regulation.
Your role
Mentees will take ownership of a well-scoped sub-task within this project, to be determined at the start based on their skills and interests. We provide the overall modeling framework, literature anchors, and iterative review.
Prerequisites
- Quantitative modeling skills (economics, statistics, or applied math).
- Familiarity with the AI risk literature (loss of control, agency).
- Strong preference for French fluency, as final policy deliverables are written directly in French.
Application question(s)
- Suggest one equation or sketch: propose a way to parameterize "degree of agency" of an AI system such that it could enter an economic value function.
- Link to a quantitative work sample.
About the mentors
Jérémy Andréoletti is Head of Research at the General-Purpose AI Policy Lab, where he works on risk modeling, forecasting, and governance for advanced AI systems. He completed a PhD at ENS-PSL on macroevolutionary Bayesian modeling and co-founded EffiSciences to support students and researchers interested in high-impact work (AI Safety, biosecurity).
Tangui Reltgen
GPAI Policy Lab
I'm an AI researcher at the GPAI Policy Lab, a French non-profit that advises policymakers on AI safety. I'm currently finishing a project comparing models of AI R&D automation to better understand how investment translates into capability gains, and what that implies for AI timelines and takeoff speed. I was originally trained in public economics and began my career in crisis management, before transitioning into AI safety.