# Toward a new AI winter? How diffusion of technological innovation on networks leads to chaotic boom-bust cycles

## TL;DR

The paper explores how technological innovation spreads and causes boom-bust cycles, suggesting AI could face a new 'winter' due to chaotic investment patterns.

## Contribution

A novel mathematical model unifying innovation diffusion and investment dynamics to explain chaotic boom-bust cycles in technology.

## Key findings

- The model reproduces long-term trends in computing and LLM development through interconnected innovation diffusion.
- Chaotic boom-bust cycles emerge when investment or diffusion exceeds a threshold in the network.
- The model aligns with observed patterns in NFT transactions and suggests implications for AI development cycles.

## Abstract

Technological developments and the impact of artificial intelligence (AI) are omnipresent themes and concerns of the present day. Much has been written on these topics but applications of quantitative models to understand the techno-social landscape have been much more limited. We propose a mathematical model that can help understand in a unified manner the patterns underlying technological development and also identify the different regimes in which the technological landscape evolves. First, we develop a model of innovation diffusion between different technologies, the growth of each reinforcing the development of the others. The model has a variable that quantifies the level of development (or innovation, discovery) potential for a given technology. The potential, or market capacity, increases via diffusion from related technologies, reflecting the fact that a technology does not develop in isolation. Hence, the growth of each technology is influenced by how developed its neighboring (related) technologies are. This allows us to reproduce long-term trends seen in computing technology and large language models (LLMs). We then present a three-dimensional system of supply, demand, and investment which shows oscillations (business cycles) emerging if investment is too high into a given technology, product, or market. We finally combine the two models through a common variable and show that if investment or diffusion is too high in the network context, chaotic boom-bust cycles can emerge. These quantitative considerations allow us to reproduce the boom-bust patterns seen in non-fungible token (NFT) transaction data and also have deep implications for the development of AI which we highlight, such as the arrival of a new AI winter.