Peter Cappelli

The backend work to build and sustain large language models (LLMs) may need more human labor than the effort saved up front. Plus, many tasks may not necessarily require the firepower of AI when standard automation will do. 

That's the word from Peter Cappelli, a management professor at the University of Pennsylvania Wharton School, who spoke at a recent MIT event. On a cumulative basis, generative AI and LLMs may create more work for people than alleviate tasks. LLMs are complicated to implement, and "it turns out there are many things generative AI could do that we don't really need doing," said Cappelli.

While AI is hyped as a game-changing technology, "projections from the tech side are often spectacularly wrong," he pointed out. "In fact, most of the technology forecasts about work have been wrong over time." He said the imminent wave of driverless trucks and cars, predicted in 2018, is an example of rosy projections that have yet to come true.