The creator economy is revolutionizing the way in which individuals can profit from their engagement with online platforms. In this paper, we initiate the formal study of online learning in a creator economy by modeling it as a three-party game between users, a platform, and content creators. The platform interacts with creators through contracts under a principal-agent framework and with users via a recommender system. We study how the platform can jointly optimize contracts and recommendation policies in an online learning setting. We analyze return-based and feature-based contracts. Under smoothness assumptions, return-based contracts achieve regret <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Theta(T^{2/3})$</tex>. For feature-based contracts, we introduce an intrinsic dimension <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> and prove a regret bound <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(T^{(d+1)/(d+2)})$</tex>, which is tight for linear families.