Networks, and the applications they support, sometimes treat each other as strangers. By shaking things up a bit—expressing networked systems as compositions of small, pure functions and making their dataflow a first-class consideration—we can often achieve friendlier couplings across the stack, to the benefit of performance, robustness, and understandability. This approach has proved helpful in several contexts: networking algorithms learned "in situ," feeding data from deployment back into training; real-time video conferencing, especially for musicians and actors during the pandemic; image compression in a distributed network filesystem; and a serverless computing framework that lets software burst to 10,000 cores that we have used for software compilation and testing, film-scale 3D rendering, video encoding and inference, and other jobs. In ongoing work, we're building a "functional" operating system that enforces a separation between IO (declared to the OS) and computation (reproducible by default). This suggests an end-to-end argument for serverless computing, shifting the service model from “renting CPUs by the second” to “providing the unambiguously correct result of a computation.” Holding infrastructure accountable to these higher-level abstractions could permit agility and innovation on other axes.