The Commoditization of LLMs
Large Language Models (LLMs) have not only fascinated technologists and researchers but have also captivated the general public. Leading the charge, OpenAI ChatGPT has inspired the release of numerous open-source models. In this post, I explore the dynamics that are driving the commoditization of LLMs.
Low switching costs are a key factor supporting the commoditization of Large Language Models (LLMs). The simplicity of transitioning from one LLM to another is largely due to the use of a common language (English) for queries. This uniformity allows for minimal cost when switching, akin to navigating between different e-commerce websites. While LLM providers might use various APIs, these differences are not substantial enough to significantly raise switching costs.
In contrast, transitioning between different database systems involves considerable expense and complexity. It requires migrating data, updating configurations, managing traffic shifts, adapting to different query languages or dialects, and addressing performance issues. Adding long-term memory [4] to LLMs could increase their value to businesses at the cost of making it more expensive to switch providers. However, for uses that require only the basic functions of LLMs and do not need memory, the costs associated with switching remain minimal.
