Overview
The Growth and AI Transition Endogenous (GATE) model is an integrated assessment model of the impact of AI development on the economy.
To accompany our paper describing the GATE model in detail, we have developed a playground that allows interested readers to modify parameter settings and observe the model’s behavior in a wide range of scenarios.
In this documentation, we describe the following:
- A concise summary of how the GATE model is structured, implemented, and solved.
- How to use the GATE playground, and interpret its predictions.
- Explanations for playground’s default parameter settings.
If you would like to ask any questions or provide feedback about the GATE playground, you may contact us at [email protected]. You can also read our accompanying blogpost for an overview of the key results suggested by the model.
Model structure
The core dynamic in GATE is an automation feedback loop: Investments drive increases in the computation used to train and deploy increasingly capable AI systems, which in turn leads to the gradual automation of tasks currently performed by humans. This in turn increases output, which makes additional resources available for further investments into AI development. The model consists of three modules as described in the figure below:
How to interpret the GATE model’s predictions
To use the GATE playground most effectively, it’s important to understand how its predictions are meant to be interpreted, and what the model’s limitations are. Notably, the model’s predictions are not meant to represent Epoch AI’s forecasts of future AI developments and economic impacts. As with any economic model, the GATE model’s predictions are instead conditional forecasts, depending on a range of assumptions both in terms of specifications and in terms of parameter values.
In particular, GATE is most useful for analyzing the high-level qualitative dynamics of AI automation, assuming that AI capabilities improvements are solely driven by increases in physical computation and better algorithms. Thus, GATE can be used for deriving stylized facts about the economic impacts of AI automation – in contrast, its quantitative predictions are substantially more uncertain and unreliable.
It is important to note that GATE predictions may be subject to optimization errors. GATE is a complex economic model with a large number of parameters, so the model’s predictions become unreliable for certain parameter ranges due to optimization issues. It is especially important to verify if the model’s predictions are simply due to optimization problems when results are unintuitive. For example, one approach to checking this is to slightly perturb parameter settings to see if results change substantially. If you identify any such bugs, please email [email protected].
Parameters
In this documentation we provide a brief description of each parameter – for more details (including the reasoning for individual parameter estimates), please refer to our technical paper.
We split the GATE model’s parameters into three categories:
- AI and compute: Parameters pertaining to the stock of effective compute, as well as to the automation of tasks
- Technological and R&D: Parameters relating to hardware and software R&D
- General economics: Parameters that are commonly used in macroeconomic models
Additionally, we outline the three main add-ons to the core model, which account for investor uncertainty, labor reallocation (or lack thereof), and positive externalities in R&D.
AI and compute parameters
Automation parameters
Runtime compute parameters
Compute investment parameters
Technological and R&D parameters
Hardware R&D parameters
Software R&D parameters
General economics parameters
FAQ
What is a FLOP?
What is effective compute?
What is effective labor?
What if some tasks remain unautomated?
What are the most important parameters of the model?
How did you estimate the parameters for the default parameter preset?
What do the aggressive and conservative presets correspond to?
What happens after full automation?
Why don’t the model predictions for GWP growth, capital stock, or compute investment match the values of today?
Does GATE take a stance on which tasks will be automated first?
Why is the initial fraction of automated tasks nonzero?
Acknowledgements
Roles and Contributions
Ege Erdil initiated the project, developed the early prototype, and played a central role in advancing the key theoretical and modeling ideas. Andrei Potlogea contributed significantly to the technical exposition and introduced refinements to the economic model. Tamay Besiroglu coordinated the project, contributed to the writing, and ensured alignment across modeling, engineering, and writing efforts. Anson Ho provided ongoing support throughout the project, including calibration of parameter values, general model refinement, and coordinating external feedback. Jaime Sevilla contributed to both the engineering and writing, ensuring coherence between the model’s implementation and its conceptual framework. Matthew Barnett contributed to the writing and parameter settings.
Engineering and Sandbox Development
Edu Roldan provided technical support on the development of the model. Matej Vrzala contributed to the design of and implemented the interactive sandbox. Andrew Souza supported the implementation of the interactive sandbox. Robert Sandler provided design support, contributing to the usability and presentation of the sandbox interface.
We are grateful to Tyler Cowen, Chad Jones, Ben Golub, Ryan Greenblatt, Kevin Kuruc, Caroline Falkman Olsson, Anton Korinek, Daniel Kokotajlo, Lev McKinney, Daan Jujin, Zachary Brown and Dan Valentine, as well as seminar attendees at the 15th Oxford workshop on Global Priorities Research for their insights and feedback.