Nearly Every Job Will be Touched by Generative AI


A key determinant of the utility of generative AI is the level of confidence humans place in the technology and how humans adapt to it.

Eight in ten jobs will either be “exposed” to or “influenced” by artificial intelligence over the coming months and years. Particularly susceptible are occupations requiring college educations. The advent of generative AI makes the issue all the more important.

That’s the word from a paper published by a team of researchers from OpenAI, OpenResearch, and the University of Pennsylvania. The study looks at the potential impact of generative AI or large language models (they use the two terms interchangeably), and estimates around 80% of the U.S. workforce could have at least 10% of their work tasks affected by AI, while approximately 19% of workers may see at least 50% of their tasks impacted. Importantly, the researchers note, “considering each job as a bundle of tasks, it would be rare to find any occupation for which AI tools could do nearly all of the work.”

The potential impact of automation – AI and robots – on managers was previously explored Lynn Wu, professor at The Wharton School and AIB Affiliated Faculty, in a study and podcast discussion in 2020.  That’s because as AI and robotics expands within a business, managers can oversee a wider breadth of operations. At the same time, AI and robots boost employment with both low-skilled and high-skilled jobs.

“Higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software,” the researchers state in the current OpenAI report. They go on to assume that at between 47% and 56% of all tasks could be completed more effectively with AI. Furthermore, their analysis suggests “that individuals holding Bachelor’s, Master’s, and professional degrees are more exposed to LLMs and LLM-powered software than those without formal educational credentials.” In addition, “the jobs with the least exposure require the most training, potentially offering a lower payoff (in terms of median income) once competency is achieved. Conversely, jobs with no on-the-job training required or only internship/residency required appear to yield higher income but are more exposed to LLMs.”

See also: Reports of the AI-Assisted Death of Prose are Greatly Exaggerated

Jobs likely to be impacted by generative AI include interpreters and translators, survey researchers, writers, animal scientists, public relations specialists, mathematicians, tax preparers, financial quantitative analysts, and web and digital interface designers, the study shows.

Still, human factors could intervene and alter these predictions, the researchers warn. “Widespread adoption of these models requires addressing existing bottlenecks. A key determinant of their utility is the level of confidence humans place in them and how humans adapt their habits. For instance, in the legal profession, the models’ usefulness depends on whether legal professionals can trust model outputs without verifying original documents or conducting independent research.”

The cost and flexibility of the technology, worker and firm preferences, and incentives also significantly influence the adoption of tools built on top of LLMs, the researchers add. “In this way, adoption may be driven by progress on some of the ethical and safety risks associated with LLMs: bias, fabrication of facts, and misalignment, to name a few. Moreover, the adoption of LLMs will vary across different economic sectors due to factors such as data availability, regulatory environment, and the distribution of power and interests.”


About Joe McKendrick

Joe McKendrick is RTInsights Industry Editor and industry analyst focusing on artificial intelligence, digital, cloud and Big Data topics. His work also appears in Forbes an Harvard Business Review. Over the last three years, he served as co-chair for the AI Summit in New York, as well as on the organizing committee for IEEE's International Conferences on Edge Computing. (full bio). Follow him on Twitter @joemckendrick.

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