IN THE SHADOW OF LLMS: TROUBLE IN THE “SMART” AUTOMOTIVE INDUSTRY

Resumen

This paper explores the tumultuous landscape of the modern automotive industry, once heralded as the epitome of artificial intelligence (AI) and machine learning (ML) applications through the promise of connected and autonomous vehicles (CAVs). The introduction of large language models (LLMs), notably with ChatGPT's launch in November 2022, marked a turning point, casting shadows over the ambitious goals set by major tech and automotive players. Notable failures, including Ford's shutdown of Argo AI and Volkswagen's struggle with electric vehicle transitions, have led to a crisis in the viability of connected and autonomous driving. The paper identifies four central types of limitations contributing to this crisis: technical, economic, financial, and regulatory. Technical limitations expose the inadequacies of autonomous vehicles in meeting their promised capabilities. Economic limitations highlight the unsustainable nature of platformizing automotive operations, linked to innovations like vehicle subscription models. Financial limitations point to the speculative investment decisions that underpin technological projects. Regulatory limitations showcase the industry's renegotiation of conditions in response to safety concerns. By examining these limitations, the paper establishes a conceptual framework applicable not only to the automotive industry but also to current discussions surrounding LLMs. The lessons drawn from these challenges contribute to a broader understanding of the perils and limitations within AI sub-industries.