We argue that the current productivity frontier of expert-LLM collaboration is doubly jagged: it is uneven, not only because of jagged AI capabilities but also due to jagged AI risks. As LLM systems improve, incompetence-driven risks (e.g., Misinformation) may decline, but adversarial risks can persist or even increase, leading to the likely persistence of a jagged risk–reward frontier. To show jaggedness, we introduce the first occupation-level quantification of risk-aware productivity gains for expert–LLM collaboration over a realistic task distribution, using standard O*NET computer-occupation tasks. For each task, we leverage six frontier models (e.g., GPT-5, Claude Opus) to produce structured risk–reward ratings and use 6 human experts to verify a subset for reliability, achieving high agreement. Risk captures the possible increase in individual, organizational, or societal harm due to employing LLMs. The reward captures potential productivity gains, i.e., time/cost savings at a fixed quality target, after accounting for expert oversight to detect and mitigate errors and risks. We aggregate task-level ratings into occupation-level scores. Our analysis shows that risk varies more than reward, yielding vast differences in risk–reward tradeoff: In safety-critical roles (e.g., Information Security Engineers), gains are offset by risk, whereas in less safety-critical roles (e.g., Web Developers), gains often substantially exceed risks. At the task-level, we identify high-reward and medium-risk tasks as the "Sweet spot" for expert-LLM collaboration. The identified sweet spot is evident in real-world deployments as the majority of Claude conversations assigned to O*NET tasks (from the recent Anthropic Economic Index) are high-reward and medium-risk, whereas high-risk tasks are minimally represented, supporting our methodology.