A recent paper looking into the economic impact of large language models found that the legal industry has the most potential occupational exposure from AI including augmentation or substitution.
What is AI occupational exposure based on?
The AIOE measure was constructed by linking 10 AI applications (abstract strategy games, realtime video games, image recognition, visual question answering, image generation, reading comprehension, language modeling, translation, speech recognition, and instrumental track recognition) to 52 human abilities (e.g., oral comprehension, oral expression, inductive reasoning, arm-hand steadiness, etc) using a crowd-sourced matrix that indicates the level of relatedness between each AI application and human ability. Data on the AI applications come from the Electronic Frontier Foundation (EFF) which collects and maintains statistics about the progress of AI across multiple applications. Data on human abilities comes from the Occupational Information Network (O*NET) database developed by the United States Department of Labor. O*NET uses these 52 human abilities to describe the occupational makeup of each of 800+ occupations that it tracks. Each of 800+ occupations can be thought of as a weighted combination of the 52 human abilities. O*NET uses two sets of weights: prevalence and importance.
It seems a bit impractical to base the degree to which AI could disrupt an industry based on a list of occupational skills assigned to a category. Maybe it makes sense if you compare within the same score (telemarketers is on the opposite end of the spectrum from pressers).
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AI reduces the cost of certain tasks to effectively zero. In doing so, it lowers the barriers to domains that would previously take years to build skills such as writing code, data analysis, and more. This is precisely why AI also increases the value of expertise and experience.