Good question! Basically, (1) most faster reactions to demand/supply changes, so can quickly move prices to an efficient level; (2) better price discrimination across individuals/markets.

For more details, please see the paper on SSRN or FRBSF (shorturl.at/x2VHn). All comments are welcomed, and follow-up research papers are on the way! Thank you all for reading!🫡🫡🫡

We show that these empirical observations can be rationalized by a simple model where a monopolist firm with incomplete information about the demand function invests in AI pricing to acquire information. Here is a picture of how our model explains the data!

Moreover, firms that adopted AI pricing experienced faster growth in sales, employment, assets, and markups, and their stock returns are also more sensitive to high-frequency monetary policy surprises than non-adopters.

At the firm level, larger and more productive firms are more likely to adopt AI pricing.

At the aggregate level, the share of AI pricing jobs in all pricing jobs has increased by more than tenfold since 2010. The increase in AI pricing jobs has been broad-based, spreading to more industries than other AI jobs.

In this paper, we document key stylized facts about the time-series trends and cross-sectional distributions of AI pricing and study its implications for firm performance using the universe of online job posting data from Lightcast.

First Post! New Paper Alert: "The Rise of AI Pricing: Trends, Driving Forces, and Implications for Firm Performance." (ssrn.com/abstract=500...) with @jonathanjadams.com, Zheng Liu, and Yajie Wang. We are excited to share it with anyone jointly interested in AI and pricing! #EconSky