by jibal | 2025-07-17 19:35:33+00:00
Dawkins and Dennett made frequent mention of each other's ideas ... e.g., Dennett discussed "selfish genes" in his book Darwin's Dangerous Idea, and elsewhere.

As for your question: the intentional stance allows us to predict the behavior of goal-driven systems in terms of what we would expect a rational agent with those goals to do ... but LLMs are not goal-driven oriented systems, and not only isn't it necessary to treat them as such but doing so leads to erroneous expectations. Here's something that you will never see an LLM do but we see humans do all the time: offer an unsolicited response saying that they realized that they made a mistake, or that they have an improvement on their previous argument ... humans do that because they are driven to get things right or to come out ahead in an argument or just to engage in conversation for various reasons... LLMs have no drives, they just match text. An LLM may be able to do great on an LSAT, but it won't pursue a career in law ... it just isn't that sort of thing.

Here's what Claude said about it (I think it got it right):

The Intentional Stance Overstates LLM Agency

Dennett's intentional stance involves treating systems as rational agents with beliefs, desires, and intentions when this proves useful for prediction and explanation. While this might seem to fit LLMs—we naturally say things like "GPT-4 believes X" or "Claude wants to be helpful"—this framing is misleading for several reasons. LLMs lack the continuity of goals and persistent world-models that characterize genuine intentional systems. When an LLM generates a response, it's not pursuing long-term objectives or maintaining beliefs across conversations. Each response emerges from pattern matching against training data, not from reasoning about how to achieve desired outcomes. The "beliefs" we might attribute to an LLM are actually statistical regularities in text, not representations the system uses to guide action.

The Design Stance Captures What's Actually Happening

The design stance, by contrast, explains behavior by reference to what a system was designed to do and how it was built to function. This fits LLMs perfectly: they were designed to predict and generate text that resembles human language patterns. Their behavior emerges from their training objective (next-token prediction), their architecture (transformer networks), and their training data. When we adopt the design stance toward LLMs, we correctly understand that their seemingly intelligent responses result from sophisticated pattern recognition and statistical inference over vast text corpora. They're not reasoning about beliefs or pursuing goals—they're executing the functions they were designed to perform. This explains both their capabilities (remarkable fluency in text generation) and their limitations (inconsistency, hallucination, lack of genuine understanding).

Why This Matters

The design stance helps us maintain appropriate expectations about what LLMs can and cannot do, while the intentional stance risks anthropomorphizing systems that, however sophisticated, remain fundamentally different from minds with genuine intentionality.