LAUSR

Significance The development of large language models has given rise to emerging markets where companies offer models as a service and compete for user usage. A concern is that the accumulation of data and compute by incumbents creates insurmountable barriers to entry for new companies. We develop a multi-objective high-dimensional regression framework to study market entry, focusing on a phenomenon which challenges this intuition. Our framework captures the reputational damage that companies face due to models’ safety violations. We show how the incumbents face greater threat of reputational damage than new companies, which reduces the amount of data the new company needs to enter the market. We quantify this reduction as a function of the incumbent’s data size.