This model simulates the tradeoff between AI costs in resources and the benefits of increased efficiency and effectiveness over time, using adaptive learning principles. It demonstrates how AI development evolves through different stages early, growth, and mature -with changing rates of investment, efficiency gains, and resource utilization.

The model tracks how AI systems stabilize over time as efficiency gains become harder to achieve, leading to a more mature and balanced system of investment and performance. It aims to provide insights into real-world AI dynamics, showing how resources translate into efficiency improvements and how diminishing returns, learning saturation, and reinforcement learning affect long-term growth.