GoKawiilWe created a web game to demonstrate the economics of AI research and development. You play an AI lab working to bootstrap an artificial superintelligence from scratch, investing labor, compute, and data into R&D until you are able to achieve self-sustaining acceleration.
The game is inspired by a recent paper, The Economics of Recursive Self-Improvement , as well as other foundational research papers from economics and computer science.
The game is built on the actual economic models from those papers, but is not meant to be a realistic forecast. The models depend heavily on their parameterization, and in the game, the parameters are calibrated for pedagogy rather than predictive accuracy. To understand how the course of the future could depend on some of the relevant parameters, we created an explorer to dig deeper into the underlying models.
AI development is complex, fast-moving, and hard to predict, but it has obeyed some statistical laws (particularly the scaling laws governing model training) with surprising fidelity. We are excited about the potential for games and simulators to help us find and understand new useful models for the trajectory of AI research.
Background
Understanding the trajectory of AI capabilities is one of the most important questions for predicting the future. In particular, understanding how AI itself accelerates AI research—often called recursive self-improvement —might be the most important component to understand, since it could lead to sharp inflection points in the rate of improvement.
We are interested in ways to quantify recursive self-improvement and predict its trajectory, and are particularly excited about the Economics of Recursive Self-Improvement paper, which came from a recently-formed group of economists (including Tom Cunningham at METR ) called the Elasticity Institute . We’re excited about their approach, and created the game and explorer to help understand the model and some of its implications more intuitively.
The explorer provides an interface for visualizing and interacting with all of the models in the paper. The game draws on ideas from the paper to create a dynamic model that also incorporates ideas from compute-optimal training , R&D-based models of growth , scale-dependent algorithmic progress , and weak-links in automation .
Takeaways
The game and explorer are tools that can be helpful for understanding the inputs and constraints of recursive self-improvement. Here we share a few insights gained from engaging with the work mentioned above and developing these tools:
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