  Until recently, search engines were the predominant method for people to
access online information. The recent emergence of large language models (LLMs)
has given machines new capabilities such as the ability to generate new digital
artifacts like text, images, code etc., resulting in a new tool, a generative
search engine, which combines the capabilities of LLMs with a traditional
search engine. Through the empirical analysis of Bing Copilot (Bing Chat), one
of the first publicly available generative search engines, we analyze the types
and complexity of tasks that people use Bing Copilot for compared to Bing
Search. Findings indicate that people use the generative search engine for more
knowledge work tasks that are higher in cognitive complexity than were commonly
done with a traditional search engine.
