The corresponding databases used in this analysis can be found in a public GitHub repository: We presented the current state of the ecosystem and community around the software packages xtb and tblite as well as the xTB methods. Within five years, the GFN2-xTB method already enabled thousands of publications, demonstrating its potential to be a new de facto standard method for computational chemistry. A crucial contribution to this success is the wide availability of the xTB methods through open source program simulation packages, mainly provided by interfacing with the xtb package, integrating the tblite library, or directly using the xtb package. The xTB methods make it possible to cover a wide range of tasks in computational chemistry, like geometry optimizations, frequency analysis, free energy computations, solvent modelling, and molecular dynamics to highlight a few. More specialized applications, like reparametrization or multiscale modelling, are supported and readily available. Special attention should be given to the crucial role of xTB methods in large scale data generation campaigns where xTB methods are present as initial screening step, intermediate refinement, or even for providing data labels, which makes it crucial in development of data sets purposed to train large scale ML models.