Elasticity Team Proposes Formal Framework for AI Economics
soumitrashukla9 · x · 2026-07-14
Elasticity, a new research collective backed by METR and Constellation, published a paper on AI economics. The team uses graphical models to provide a clear, minimal formalization of key factors related to recursive self-improvement (RSI).
The research isolates key elasticity and hyperelasticity metrics that need measuring:
- Core bottlenecks: Data, inference, and compute for experiments and training.
- Capability divergence: The disconnect between the acceleration of narrow domain skills and general capability improvements.
- Algorithmic acceleration: Capability leaps driven by specific algorithms.
- Economic feedback loops: How commercial benefits reinvest into technological development.
Furthermore, the study points out that the biggest current uncertainty is the impact of model scale on algorithmic progress: how much faster can algorithmic discoveries be made when effective model scale increases by 10x? This metric is currently hard to measure precisely and may continuously grow over time.
Related event: Elasticity Institute's First Paper: Formalizing Recursive Self-Improvement(5 posts)→
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