Ali Merali is a PhD candidate in the Yale Department of Economics and a Forethought Fellow whose research sits at the intersection of economic theory and artificial intelligence. His primary work involves running preregistered randomized controlled trials to derive empirical scaling laws for economic outcomes — measuring how increases in LLM training compute translate into improvements in worker productivity, earnings, and task quality. He has previously conducted research at the Global Priorities Institute, Epoch AI, and the Center on Long-Term Risk, and has held roles at OpenAI, Google DeepMind, and the Centre for the Governance of AI (GovAI).
Funding Details
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- Funding Goal
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- Funding Raised to Date
- $54,650
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Theory of Change
Merali's theory of change is that rigorous empirical measurement of how AI model scaling affects economic outcomes is essential for forecasting the trajectory and pace of transformative AI. By establishing quantitative scaling laws for economic productivity — showing how much each doubling of compute improves real worker output — his research provides policymakers, funders, and safety researchers with grounded evidence for anticipating when and how AI could drive explosive economic growth. This in turn supports better preparation for the governance and safety challenges that transformative AI would bring.
Grants Received
from Open Philanthropy
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Details
- Last Updated
- Apr 2, 2026, 9:51 PM UTC
- Created
- Mar 20, 2026, 2:34 AM UTC