Vibe Coding, Interface Flattening

TL;DR

This paper conceptualizes vibe coding as interface flattening, where diverse programming modalities converge into a conversational surface, revealing how LLMs reshape control, power, and literacy in software development.

Contribution

It offers a materialist analysis of vibe coding as interface flattening, highlighting infrastructural influences on control and the political economy of AI-mediated programming.

Findings

Control shifts to model and protocol providers due to infrastructural dependencies.

Vibe coding redistributes symbolic labor and obscures responsibility.

It reveals new dependencies and literacies in AI-mediated development.

Abstract

Large language models are reshaping programming by enabling 'vibe coding': the development of softwares through natural-language interaction with model-driven toolchains. This article argues that vibe coding is best understood as interface flattening, a reconfiguration in which previously distinct modalities (GUI, CLI, and API) appear to converge into a single conversational surface, even as the underlying chain of translation from intention to machinic effect lengthens and thickens. Drawing on Friedrich Kittler's materialist media theory and Alexander Galloway's account of interfaces as sites of protocol control, the paper situates programming as a historically localised interface arrangement rather than an essential relation to computation. Through a materialist reconstruction of the contemporary vibe-coding stack, it shows how remote compute infrastructures, latency and connectivity, structured outputs, function/tool calling, and interoperability standards such as the Model Context Protocol relocate control and meaning-making power to model and protocol providers. The apparent democratisation of technical capability therefore depends on new dependencies and new literacies. By foregrounding the tension between experiential flattening and infrastructural thickening, I demonstrate how LLM-mediated development redistributes symbolic labour/power, obscures responsibility, and privatises competencies previously dispersed across programming communities, contributing a critical lens on the political economy of AI-mediated human-computer interaction.