Fostering a rigorous
understanding of the
future of AI

Our approach

Epoch is a multidisciplinary research institute investigating the trajectory of Artificial Intelligence (AI). We scrutinize the driving forces behind AI and forecast its ramifications on the economy and society.

We emphasize making our research accessible through our reports, models and visualizations to help ground the discussion of AI on a solid empirical footing. Our goal is to create a healthy scientific environment, where claims about AI are discussed with the rigor they merit.

Our research covers the following areas:

Trends in Machine Learning

We conduct in-depth analyses on compute, data, and investment trends to solidify our understanding of AI's trajectory.

Visit Trends page

Economics of AI automation

We build models to understand the economic drivers and impacts of AI automation.

Open takeoff model playground

Algorithmic progress

We investigate how innovations in AI are allowing us to build more capable models with fewer resources.

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Data in Machine Learning

We research the challenges and solutions related to data bottlenecks that AI labs may encounter.

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Highlighted research


Compute Trends Across Three Eras of Machine Learning

We compile a dataset of the training compute for over 120 Machine Learning models, highlighting novel trends and insights into the development of AI since 1952, and what to expect going forward.


Revisiting Algorithmic Progress

We use a dataset of over a hundred computer vision models from the last decade to investigate how better algorithms and architectures have enabled researchers to use compute and data more efficiently. We find that every 9 months, the introduction of better algorithms contribute the equivalent of a doubling of compute budgets.


Will We Run Out of ML Data? Evidence From Projecting Dataset Size Trends

Based on our previous analysis of trends in dataset size, we project the growth of dataset size in the language and vision domains. We explore the limits of this trend by estimating the total stock of available unlabeled data over the next decades.

Research resources

AI Trends
A collection of key data from our research on Machine Learning.
Parameters, Compute and Data
A visualization of the key characteristics of milestone Machine Learning models since the 1950s. It showcases information from a curation of over 500 models that fit a notability criteria.
Interactive model
Direct Approach Interactive Model
The Direct Approach framework bounds the compute requirements for transformative AI by extrapolating neural scaling laws. We use those estimates to produce a user-adjustable forecast over the date at which TAI will be achieved.
Interactive model
Interactive Model of AI Takeoff Speeds
We have developed an interactive website showcasing a new model of AI takeoff speeds.
Interactive tool
Estimating Training Compute of Deep Learning Models
Use our interactive calculator to estimate the amount of compute that was used to train a Machine Learning model.