Consensus-based optimization (CBO) is a derivative-free optimization method driven by interacting particles and multiplicative noise. Its diffusion degenerates at consensus, and every consensus configuration is an equilibrium. These features prevent a direct application of standard approaches to uniform-in-time propagation of chaos based on uniform ellipticity or global contractivity. I will present quantitative uniform-in-time propagation of chaos results for first-order and second-order CBO under explicit parameter and moment conditions. The proof exploits dissipation in centered variables and a refined stability estimate for the weighted consensus point. Exponential decay of centered moments, together with concentration estimates, makes the error coefficients integrable in time and yields the classical Monte Carlo rate. For the original first-order model, this requires neither additional confinement nor a bounded-domain cutoff. I will then discuss the asymptotic shape of the concentrating mean-field density for a dynamically regularized first-order CBO model. Beyond convergence to a Dirac mass, we identify a nontrivial limiting profile after centering and rescaling, and establish convergence in L1. The proof combines Wasserstein convergence with weighted Fisher information estimates that control the regularity of the rescaled density. The talk is based on joint work with Nicolai Gerber, Seung-Yeal Ha, Franca Hoffmann, Wuchen Li, and Urbain Vaes.