LUCR - Linking Utility and Compute Rate
Matthew Duffy · Team LUCR Team
Submitted to The AI Forecasting Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.
My project, LUCR (Linking Utility and Compute Rate), is an open-source forecasting model that quantifies "algorithmic efficiency". I built a two-stage Bayesian model to bridge Epoch's compute-capability data with METR's "subhuman" task benchmarks. The model's key output is the LUCR metric (dMETR / d(log C)), which treats algorithmic improvement as a measurable variable. This provides a policy-relevant tool to generate AGI timeline probabilities under "what-if" scenarios, like an "AGI Race" or "Compute Restrictions". The full pipeline is open-sourced and reproducible.
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Cite this project
@misc{duffy2025lucr,
title = {{LUCR - Linking Utility and Compute Rate}},
author = {Matthew Duffy},
year = {2025},
month = nov,
note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/lucr-linking-utility-and-compute-rate-rzeg}},
url = {https://apartresearch.com/sprints/projects/lucr-linking-utility-and-compute-rate-rzeg}
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