AI models, especially deep learning-based ones, tend to perform well in structured chaos—environments where there is a high volume of data with underlying but complex patterns (e.g., financial markets, weather forecasting, protein folding). However, they struggle in true chaos—situations where outcomes are highly sensitive to initial conditions (like turbulence or certain geopolitical events) and lack sufficient historical patterns to learn from.
Chaos makes prediction harder when:
1. The system is highly nonlinear and sensitive to small changes.
2. There’s a lack of sufficient training data.
3. The system undergoes structural breaks (e.g., black swan events).
AI is good at detecting statistical regularities but less effective when those regularities don’t hold over time.
Via ChatGPT