Announcing Epoch: A research organization investigating the road to Transformative AI

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  • We are a new research organization working on investigating trends in Machine Learning and forecasting the development of Transformative Artificial Intelligence

  • This work is done in close collaboration with other organizations, like Rethink Priorities, Open Philanthropy, and MIT CSAIL

  • We will be hiring for 2-4 full-time roles this summer – more information here

  • You can find up-to-date information about Epoch on our website

What is Epoch?

Epoch is a new research organization that works to support AI strategy and improve forecasts around the development of Transformative Artificial Intelligence (TAI) – AI systems that have the potential to have an effect on society as large as that of the industrial revolution.

Our founding team consists of seven members – Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Pablo Villalobos, Eduardo Infante-Roldán, Marius Hobbhahn, and Anson Ho. Collectively, we have backgrounds in Machine Learning, Statistics, Economics, Forecasting, Physics, Computer Engineering, and Software Engineering.

Our work involves close collaboration with other organizations, such as MIT CSAIL, Open Philanthropy, and Rethink Priorities’ AI Governance and Strategy team. We are advised by Tom Davidson from Open Philanthropy and Neil Thompson from MIT CSAIL. Rethink Priorities is also our fiscal sponsor.

Our mission

Epoch seeks to clarify when and how TAI capabilities will be developed.

We see these two problems as core questions for informing AI strategy decisions by grantmakers, policy-makers, and technical researchers.

We believe that to make good progress on these questions we need to advance towards a field of AI forecasting. We are committed to developing tools, gathering data and creating a scientific ecosystem to make collective progress towards this goal.

Epoch´s website

Our research agenda

Our work at Epoch encompasses two interconnected lines of research:

  • The analysis of trends in Machine Learning. We aim to gather data on what has been happening in the field during the last two decades, explain it, and extrapolate the results to inform our views on the future of AI.

  • The development of quantitative forecasting models related to advanced AI capabilities. We seek to use techniques from economics and statistics to predict when and how fast AI will be developed.

These two research strands feed into each other: the analysis of trends informs the choice of parameters in quantitative models, and the development of these models brings clarity on the most important trends to analyze.

A sketch of Epoch’s research agenda. We plan to develop quantitative models to forecast advanced AI capabilities, and to research and extrapolate trends in Machine Learning.

Besides this, we also plan to opportunistically research topics important for AI governance where we are well positioned to do so. These investigations might relate to compute governance, near-term advances in AI and other topics.

Our work so far

Earlier this year we published Compute Trends Across Three Eras of Machine Learning. We collected and analyzed data about the training compute budget of >100 Machine Learning models across history. Consistent with our commitment to field building, we have released the associated dataset and an interactive visualization tool to help other researchers understand these trends better. This work has been featured in Our World in Data, in The Economist and at the OECD.

More recently we have published Grokking “Forecasting TAI with biological anchors” and Grokking “Semi-informative priors over AI timelines”. In these pieces, Anson Ho dissects two popular AI forecasting models. These are the two first installments of a series of articles covering work on quantitative forecasting of when we will develop TAI.

Diagram summarizing Ajeya Cotra’s biological anchors model.

You can see more of our work on our blog. Here is a selection of further work by Epoch members:

Projecting compute trends in Machine Learning

Estimating training compute of Deep Learning models

Estimating the backward-forward FLOP ratio

Parameter counts in Machine Learning


We expect to be hiring for several full-time research and management roles this summer. Salaries range from $60,000 for entry roles to $80,000 for senior roles.

If you think you might be a good fit for us, please apply! If you’re unsure whether this is the right role for you, we strongly encourage you to apply anyway. Please register your interest for these roles through our webpage.