Years ago, I read a blog post called Always Bet on Text that left a lasting impression on me. Not only did it accord with my own experience, but it gave me a good, compact reference frame to think about technology. No recent technology is more suited to this reference frame than Generative AI, for the simple reason that, at its core, modern GenAI is all about text completion.

For all the millions of words that have been devoted to the capabilities of LLMs, the economics and ergonomics of these systems seem to me to be under-discussed. In much the same way that technology changes - from records to CDs to streaming - have been a dominant factor in the evolution of popular music, prosaic matters such as cost structures will have a substantial influence on the development and impact of AI. While it is still early days for LLMs, there are already signs that economic factors are impacting everything from product design and technology choice to adoption and even regulation.

While many AI systems can be very complex, Large Language Models (LLMs) seem to be refreshingly simple to understand. With their ability to generate human-like text, applications like ChatGPT have captured the popular imagination. However, as compelling an experience as chatbots are, the tradeoffs that they make can lead us to misunderstand LLMs as a whole. In this post, we will explore the idea that the functions of LLMs can be thought of as discrete capabilities, and how different composition of these functions leads to a much wider variety of user experiences.