Since well before the advent of Generative AI, machine learning models exceeded human forecasting performance across a whole range of specific domains. Within a bounded domain with sufficient data, machine learning is often extremely good at predicting outcomes.
However, machine learning can only work within defined domains where there is sufficient data. In most real world decision-making situations their forecasts need to be taken with a high degree of caution.
One of the critical differences between most traditional analytic AI approaches and Large Language Models (LLMs) is that the former almost always applies to bounded domains, while the nature of LLMs is that their scope is unbounded. As such, it has the potential to help make better forecasts in conjunction with humans across various domains including business, economics, politics, science, and more.