Building today's massive AI models can cost hundreds of millions of dollars, with projections suggesting it could hit a staggering billion dollars within a few years. Much of that expense is for computing power from specialized chips-typically Nvidia GPUs, of which tens of thousands may be required, costing as much as $30,000 each.
Data labeling has long been used to develop AI models for self-driving cars, for example. A camera captures images of pedestrians, street signs, cars, and traffic lights and human annotators label the images with words like 'pedestrian,' 'truck,' or 'stop sign.' The labor-intensive process has also raised ethics concerns.
Today's generic large language models (LLMs) go through an exercise related to data labeling called Reinforcement Learning Human Feedback, in which humans provide qualitative feedback or rankings on what the model produces. That is one significant source of rising costs.
In addition, labeling highly technical, expert-level data in fields like legal, finance, and healthcare is driving up expenses. Companies are hiring high-cost doctors, lawyers, PhDs, and scientists to label certain data, contributing to the rising costs.
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