In my career I've focused mostly on applying what is now called 'traditional machine learning': regression, classification, time series, anomaly detection and clustering algorithms. You could frame machine learning as applying an algorithmic 'constrained intelligence' to a specific business problem. The challenge has always been to 'unconstrain the intelligence' (f.e. by tuning hyperparameters) and to further specify the business problem (proper target definition, clean data, proper cross validation schemes). The advent of large language models is starting to flip the equation; from 'unconstraining' intelligence to 'constraining' it instead.