Programming is a powerful problem-solving tool. Systems that can assist programmers or even generate programs themselves could make programming more productive and accessible. Recent large-scale language models have demonstrated impressive abilities to generate code, however they still perform poorly on more complex tasks that require problem-solving skills, such as competitive programming problems. In this talk we'll present AlphaCode, the motivations of the project and the design decisions we made. AlphaCode is a system for code generation that achieved an average ranking of top 54.3% in simulated evaluations on popular, recent programming competitions on the Codeforces platform. AlphaCode's success stemmed from: large transformer-based models, using a novel combination of architectural, training, and prompting modifications; extensive datasets; efficient large-scale sampling; and filtering and clustering-based sample selection. This marks the first time an artificial intelligence system has performed competitively in programming competitions.