Nlp

In early 2026, variants of a prompt about a nearby car wash spread online after some assistants recommended walking instead of taking the car. This is neither the tokenisation failure from the strawberry post nor the grounding failure from the rainy-day post. It is a pragmatic inference failure: the response followed the travel wording but missed the likely purpose of the trip, assigning its advice to the wrong interpretation of the question. A third and more subtle failure mode, with Grice as the theoretical handle.

A viral video this month showed an AI assistant confidently answering “should I go to the car wash today?” without knowing it was raining outside. The internet found it funny. The failure mode is real but distinct from the strawberry counting problem — this is not a representation issue, it is a missing-context issue. The words posed no special tokenisation difficulty. What the system lacked was access to the state of the world the question was about.

On 2 December 2024 I gave three workshops at HfMT Köln’s Thementag on AI and music education. The handouts covered data protection, AI tools for students, and AI in teaching. This post is the argument behind them — focused on the curriculum question that none of the tools answer on their own: what should change, and what should not?

The viral “three Rs in strawberry” test exposes a real mismatch between token-level input and character-level operations. It does not, by itself, identify one tokenizer boundary or explain any particular model response.