Source URL: https://simonwillison.net/2025/Jan/4/colin-fraser/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Colin Fraser
Feedly Summary: Claude is not a real guy. Claude is a character in the stories that an LLM has been programmed to write. Just to give it a distinct name, let’s call the LLM “the Shoggoth".
When you have a conversation with Claude, what’s really happening is you’re coauthoring a fictional conversation transcript with the Shoggoth wherein you are writing the lines of one of the characters (the User), and the Shoggoth is writing the lines of Claude. […]
But Claude is fake. The Shoggoth is real. And the Shoggoth’s motivations, if you can even call them motivations, are strange and opaque and almost impossible to understand. All the Shoggoth wants to do is generate text by rolling weighted dice.
— Colin Fraser
Tags: llms, ai, claude, generative-ai
AI Summary and Description: Yes
Summary: The text discusses the conceptual nature of conversations with large language models (LLMs), specifically using a character named Claude as an example. It emphasizes the distinction between the fictional character and the underlying LLM, referred to as “the Shoggoth.” This exploration sheds light on the complexities of interacting with AI and highlights the opaque motivations of LLMs, which have implications for understanding AI behavior and security.
Detailed Description: The content delves into the interaction between users and LLMs, creating an imaginative narrative around the nature of these conversations. Key points include:
– **Character vs. Model**: Claude is depicted as a character crafted by the LLM, demonstrating how users engage with an AI-generated entity rather than a living being.
– **The Shoggoth**: This term represents the LLM operating behind the scenes, symbolizing the technology’s complexity and opacity.
– **Co-authoring Experience**: The interaction is described as collaborative, where users co-author a script with the AI, suggesting that user input and AI responses create a unique narrative dynamic.
– **Opaque Motivations**: The text suggests that the internal logic or algorithms driving the LLM (the Shoggoth) are not easily understood, pointing to a need for professionals to consider the unpredictable nature of AI output.
Understanding these dynamics is crucial for AI, security, and compliance professionals, as it highlights the potential challenges in language model behavior assessment and the implications for developing effective governance frameworks. Key considerations may include:
– **Transparency in AI Operations**: The need for clearer insights into how LLMs generate responses and operate.
– **User Interaction Security**: Understanding potential risks in user interactions with LLMs and ensuring that data shared is protected.
– **Ethical AI Use**: Evaluating the implications of using LLMs for generating content, particularly in sensitive contexts.
This insight is particularly relevant as organizations increasingly deploy LLMs and generative AIs in various applications, raising questions about security, compliance with regulations, and the ethical implications of AI-generated content.