Predictive kinetic modeling based largely on quantum chemistry has advanced to the state where it can compete with traditional heavily empirical methods of constructing quantitative kinetic models in several subfields of chemistry. However, there is significant room for improving its scope and reliability when the challenges highlighted in this paper are overcome. A key, both today and in the future, is the continued development of methods for computing and estimating k(T) and Keq(T), with better identification of when those calculations or estimates are significantly uncertain. Achieving better estimates relies on access to more and better data both from quantum chemical calculations and from experiments. More open data sharing, and more recognition of the people who do the hard work involved in effectively sharing data and compiling curated data sets (ideally with error bars and provenance information on each number), would help us reach this goal. As the community routinely uses ever larger datasets and kinetic simulations, improved software tools are needed to assist humans in working with and checking the data, the models, and model vs. experiment comparisons, as well as better algorithms to convert the raw power of new computers into easy-to-use tools for predicting the behavior of chemical systems.