The field of AI has become implementation-bound. We have plenty of ideas, but it is increasingly laborious to try them out, as our models become more ambitious and our datasets become larger, noisier, and more heterogeneous. The software engineering burden makes it hard to start new work; hard to reuse and combine existing ideas; and hard to educate our students. In this talk, I'll propose to hide many common implementation details behind a new level of abstraction that we are developing. Dyna is a declarative programming language that combines logic programming with functional programming. It also supports modularity. It may be regarded as a kind of deductive database, theorem prover, truth maintenance system, or equation solver. I will illustrate how Dyna makes it easy to specify the combinatorial structure of typical computations needed in natural language processing, machine learning, and elsewhere in AI. Then I will sketch implementation strategies and program transformations that can help to make these computations fast and memory-efficient. Finally, I will suggest that machine learning should be used to search for the right strategies for a program on a particular workload.