Brynjolfsson et al. (2025) compare employment trends for workers in more versus less AI-exposed occupations, using the task exposure measures from Eloundou et al. (2023) and payroll data from ADP. Johnston and Makridis (2025) do a similar task-based analysis using US administrative data, but they aggregate treatment to the industry level. Hui et al. (2024) study how freelance jobs on Upwork responded to the release of ChatGPT and advanced image generation tools, comparing workers in directly affected categories to those in unaffected categories before and after each tool's release date. Hampole et al. (2025) instrument for firm-level AI adoption using historical university hiring networks: firms that historically recruited from universities whose graduates later entered AI-related roles faced lower adoption costs.
“没有海量真实场景数据的‘喂养’，再强的芯片也只是空谈。”一位从蔚来智驾部门离职的核心算法工程师向虎嗅回忆，“为了适配神玑，我们重构了底层架构，进度一度滞后，直接错失了端到端大模型落地的最佳窗口期。在模型泛化能力上，我们与拥有百万级车队的对手差距明显。”
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。关于这个话题，旺商聊官方下载提供了深入分析
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