Large language models (LLMs) change how consumers acquire information online; their bots also crawl news publishers' websites for training data and to answer consumer queries; and they provide tools that can lower the cost of content creation. These changes lead to predictions of adverse impact on news publishers in the form of lowered consumer demand, reduced demand for newsroom employees, and an increase in news "slop." Consequently, some publishers strategically responded by blocking LLM access to their websites using the robots.txt file standard. Using high-frequency granular data, we document four effects related to the predicted shifts in news publishing following the introduction of generative AI (GenAI). First, we find a moderate decline in traffic to news publishers occurring after August 2024. Second, using a difference-in-differences approach, we find that blocking GenAI bots can be associated with a reduction of total website traffic to large publishers compared to not blocking. Third, on the hiring side, we do not find evidence that LLMs are replacing editorial or content-production jobs yet. The share of new editorial and content-production job listings increases over time. Fourth, regarding content production, we find no evidence that large publishers increased text volume; instead, they significantly increased rich content and use more advertising and targeting technologies. Together, these findings provide early evidence of some unforeseen impacts of the introduction of LLMs on news production and consumption.

翻译：大语言模型（LLMs）改变了消费者在线获取信息的方式；其机器人程序会抓取新闻出版商网站以获取训练数据并回答用户查询；同时它们提供的工具能够降低内容创作成本。这些变化引发了关于新闻出版商将面临负面影响的预测，包括消费者需求下降、新闻采编人员需求减少以及新闻“劣质内容”增加。因此，部分出版商通过 robots.txt 文件标准策略性地屏蔽了 LLM 对其网站的访问。基于高频细粒度数据，我们记录了生成式人工智能（GenAI）引入后新闻出版领域预测转变相关的四种效应。首先，我们发现 2024 年 8 月后新闻出版商的网站流量出现适度下降。其次，通过双重差分法分析，我们发现大型出版商屏蔽 GenAI 机器人与其网站总流量下降存在关联。第三，在招聘方面，我们尚未发现 LLMs 取代采编或内容生产岗位的证据；相反，新增采编与内容生产岗位的占比随时间持续上升。第四，在内容生产方面，我们未发现大型出版商增加文本产量，而是显著增加了富媒体内容，并更多采用广告与定向投放技术。这些发现共同为 LLMs 引入对新闻生产与消费产生的部分未预见影响提供了早期证据。