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Figure 2 shows observed exposure (in red) compared to β from Eloundou et al. (in blue), illustrating the difference between theoretical and actual use on our platform, grouped by broad occupational categories. We calculate this by first averaging to the occupation level weighting by our time fraction measure, then averaging to the occupation category weighting by total employment. For example, the β measure shows scope for LLM penetration in the majority of tasks in Computer & Math (94%) and Office & Admin (90%) occupations.
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Most vibe coding projects start the same way. You start making code with AI, test it, fix it, and repeat until the app works. The overwhelming majority of stories on Reddit start this way, and so that seems to be the most popular process. However, doing it this way has its problems, and since vibe coding is quickly becoming so popular, many of the same mistakes keep appearing.
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