We investigate the scalability of AI training runs. We identify electric power, chip manufacturing, data and latency as constraints. We conclude that 2e29 FLOP training runs will likely be feasible by 2030.

By Jaime Sevilla, Tamay Besiroglu, Ben Cottier, Josh You, Edu Roldán, Pablo Villalobos, and Ege Erdil

Our expanded AI model database shows that the compute used to train recent models grew 4-5x yearly from 2010 to May 2024. We find similar growth in frontier models, recent large language models, and models from leading companies.

By Jaime Sevilla and Edu Roldán

We combine the Direct Approach framework with simple models of progress in algorithms, investment, and compute costs to produce a user-adjustable forecast of when TAI will be achieved.

By David Atkinson, Matthew Barnett, Edu Roldán, Ben Cottier, and Tamay Besiroglu

We have developed an interactive website showcasing a new model of AI takeoff speeds.

By Jaime Sevilla and Edu Roldán