The mission of the AI Index is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI. To achieve this, we track, collate, distill, and visualize dat| hai.stanford.edu
AI supercomputers double in performance every 9 months, cost billions of dollars, and require as much power as mid-sized cities. Companies now own 80% of all AI supercomputers, while governments’ share has declined.| Epoch AI
We’ve compiled a comprehensive dataset of the training compute of AI models, providing key insights into AI development.| Epoch AI
FrontierMath is a benchmark of hundreds of unpublished and extremely challenging math problems to help us to understand the limits of artificial intelligence.| Epoch AI
We’ve expanded our Biology AI Dataset, now covering 360+ models. Our analysis reveals rapid scaling from 2017-2021, followed by a notable slowdown in biological model development.| Epoch AI
We investigate four constraints to scaling AI training: power, chip manufacturing, data, and latency. We predict 2e29 FLOP runs will be feasible by 2030.| Epoch AI
If trends continue, language models will fully utilize the stock of human-generated public text between 2026 and 2032.| Epoch AI
We characterize techniques that induce a tradeoff between spending resources on training and inference, outlining their implications for AI governance.| Epoch AI
