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：大型语言模型（LLMs）改变了消费者在线获取信息的方式；其机器人程序会爬取新闻出版商的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。这些变化导致了对新闻出版商的负面影响的预测，包括消费者需求下降、新闻采编人员需求减少以及新闻“低质内容”的增加。因此，部分出版商通过采用robots.txt文件标准来策略性地阻止LLM对其网站的访问。利用高频细粒度数据，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个效应。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，我们发现相较于未采取屏蔽措施的出版商，屏蔽GenAI机器人可能与大型出版商网站总流量减少相关。第三，在招聘方面，我们尚未发现LLMs正在取代采编或内容生产岗位的证据。新增采编与内容生产岗位的招聘比例随时间推移呈上升趋势。第四，在内容生产方面，我们没有发现大型出版商增加文本产量的证据；相反，它们显著增加了富媒体内容，并更多地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某些未预见影响提供了早期证据。