Training ML models until low error rates are achieved requires billions of $ invested

For example, achieving a 5 percent error rate would require 10 19 billion floating-point operations.

Important work by scholars at the University of Massachusetts Amherst allows us to understand the economic cost and carbon emissions implied by this computational burden. The answers are grim: Training such a model would cost US $100 billion and would produce as much carbon emissions as New York City does in a month. And if we estimate the computational burden of a 1 percent error rate, the results are considerably worse

A large European supermarket chain recently abandoned a deep-learning-based system that markedly improved its ability to predict which products would be purchased. The company executives dropped that attempt because they judged that the cost of training and running the system would be too high.

Source https://spectrum.ieee.org/deep-learning-computational-cost