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Arthur T Knackerbracket [soylentnews.org] writes:

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Arthur T Knackerbracket has processed the following story [zdnet.com]:

There's common agreement that generative artificial intelligence [zdnet.com] (AI) tools can help people save time and boost productivity. But while these technologies make it easy to run code [zdnet.com] or produce reports quickly, 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 [upenn.edu], management professor at the University of Pennsylvania Wharton School, who spoke at a recent MIT event [mit.edu]. 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 [zdnet.com], "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. 

Broad visions of technology-driven transformation often get tripped up in the gritty details. Proponents of autonomous vehicles [zdnet.com] promoted what "driverless trucks could do, rather than what needs to be done, and what is required for clearing regulations -- the insurance issues, the software issues, and all those issues." Plus, Cappelli added: "If you look at their actual work, truck drivers do lots of things other than just driving trucks, even on long-haul trucking."

A similar analogy can be drawn to using generative AI for software development [zdnet.com] and business. Programmers "spend a majority of their time doing things that don't have anything to do with computer programming," he said. "They're talking to people, they're negotiating budgets, and all that kind of stuff. Even on the programming side, not all of that is actually programming."  

The technological possibilities of innovation are intriguing but rollout tends to be slowed by realities on the ground. In the case of generative AI, any labor-saving and productivity benefits may be outweighed by the amount of backend work needed to build and sustain LLMs and algorithms. 

Both generative and operational AI "generate new work," Cappelli pointed out. "People have to manage databases, they have to organize materials, they have to resolve these problems of dueling reports, validity, and those sorts of things. It's going to generate a lot of new tasks, somebody is going to have to do those."

He said operational AI that's been in place for some time is still a work in progress. "Machine learning with numbers has been markedly underused. Some part of this has been database management questions. It takes a lot of effort just to put the data together so you can analyze it. Data is often in different silos in different organizations, which are politically difficult and just technically difficult to put together."  

Cappelli cites several issues in the move toward generative AI and LLMs that must be overcome:

Cappelli suggested the most useful generative AI application in the near term is sifting through data stores and delivering analysis to support decision-making processes. "We are washing data right now that we haven't been able to analyze ourselves," he said. "It's going to be way better at doing that than we are," he said. Along with database management, "somebody's got to worry about guardrails and data pollution issues."

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