Link to original study: …amazonaws.com/2024-01-18+Beyond_AI_Exposure.pdf

In this paper, we address three important shortcomings of AI exposure models to construct a more economically-grounded estimate of task automation. First, we survey workers familiar with end-use tasks to understand what performance would be required of an automated system. Second, we model the cost of building AI systems capable of reaching that level of performance. This cost estimate is essential to understanding the deployment of AI, since technically-exacting systems can be enormously expensive. And third, we model the decision about whether AI adoption is economically-attractive. The result is the first end-to-end AI automation model. A simple hypothetical example makes clear why these considerations are so important. Consider a small bakery evaluating whether to automate with computer vision. One task that bakers do is to visually check their ingredients to ensure they are of sufficient quality (e.g. unspoiled). This task could theoretically be replaced with a computer vision system by adding a camera and training the system to detect food that has gone bad. Even if this visual inspection task could be separated from other parts of the production process, would it be cost effective to do so? Bureau of Labor Statistics O*NET data imply that checking food quality comprises roughly 6% of the duties of a baker. A small bakery with five bakers making typical salaries ($48,000 each per year), thus has potential labor savings from automating this task of $14,000 per year. This amount is far less than the cost of developing, deploying and maintaining a computer vision system and so we would conclude that it is not economical to substitute human labor with an AI system at this bakery.