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EXCERPT:
A new working paper by Philip Moreira Tomei and Bouke Klein Teeselink, posted to arXiv in early May, makes claims that, if correct, should reorient the workforce policy conversation. In short, the authors argue that the AI exposure indices that have shaped most current thinking are looking at an incomplete subset of digital work. They identify which jobs and tasks current language models can already accelerate but they miss the jobs with features that make them amenable to automation later.
Tomei and Klein Teeselink build a new index that scores all 17,951 task statements in the federal O*NET database. The authors propose a measure what they call “reinforcement learning feasibility” which asks whether a task has the structural features (e.g., verifiable outcomes, use environments amenable to simulation, discrete decision/feedback loops) that allow AI systems to be trained on it through the post-training methods that are becoming the main drivers of AI capability. They then compare their index to the most-cited existing measure, from Eloundou and colleagues, which looks at whether tasks can be automated with current technology.
