AI Telephone Pictionary: How LLMs and VLMs Are Redefining Digital Creativity

AI Telephone Pictionary: How LLMs and VLMs Are Redefining Digital Creativity

In a novel experiment that has captivated the AI research and enthusiast communities, software engineer and AI hobbyist /u/jfowers_amd recently conducted a multi-modal "telephone pictionary" game using a chain of artificial intelligence systems—including large language models (LLMs), vision language models (VLMs), text-to-image generators like Stable Diffusion (SD), and the lesser-known Kokoro model—running locally on an AMD Strix Halo gaming rig. The result: a cascading series of visual and textual distortions that transformed a simple prompt into an abstract, almost surreal collage of misinterpreted imagery and semantic drift.

The game began with a human-provided phrase: "a cat wearing a top hat and monocle, drinking tea in a Victorian library." This prompt was fed to an LLM, which generated a descriptive paragraph. That paragraph was then given to a VLM, which attempted to generate an image based on the text. The resulting image was then described back into text by another LLM, and the cycle repeated through SD and Kokoro, each model interpreting the output of the previous without access to the original context. After five iterations, the final image bore little resemblance to the original prompt—instead depicting a feline-like creature with elongated limbs, hovering above a floating teacup, surrounded by floating gears and distorted bookshelves.

This experiment, while playful, underscores a critical insight into the nature of multimodal AI: each model operates within its own semantic space, translating inputs through internal representations that are not grounded in human intention. As noted in linguistic analyses of similar recursive communication tasks, the phrase "playing with"—when applied to AI systems—implies a dynamic, iterative exchange rather than a static interaction. According to linguistic research on verb usage in human-AI contexts, "playing with" suggests engagement with tools or systems that respond unpredictably, unlike "playing someone," which implies deception (as discussed in ELL StackExchange). In this case, the AI systems are not being "played"; they are being played with—a subtle but crucial distinction.

The technical setup on the Strix Halo, a high-end consumer GPU platform, enabled local inference without cloud dependencies, ensuring data privacy and reducing latency. This is significant, as most AI experiments of this nature rely on cloud APIs, which introduce external variables like rate limiting and proprietary preprocessing. By running everything locally, the experimenter isolated the phenomenon to model architecture and training data alone.

The distortion observed is not a bug—it’s a feature of emergent behavior in neural networks. Each model compresses and reconstructs information based on its training corpus. LLMs prioritize linguistic coherence; VLMs prioritize visual plausibility; SD optimizes for aesthetic appeal over semantic fidelity. Kokoro, a specialized model for stylized anime-style generation, introduced its own stylistic bias, further amplifying divergence. The cumulative effect is what researchers call "semantic entropy": the gradual loss of meaning through iterative translation.

While some dismiss the experiment as a tech demo or meme, others see it as a powerful metaphor for how information degrades in digital ecosystems—from social media chains to automated content moderation pipelines. The experiment serves as a cautionary tale: when AI systems relay information without grounding in original intent, the result is not just humor, but potential misinformation.

As AI becomes more integrated into creative workflows, understanding these drift mechanisms is essential. Artists, designers, and journalists must recognize that AI-generated content is not a transparent conduit—it’s a filter, a translator, and sometimes, a misinterpreter. The telephone pictionary experiment, though whimsical, offers a vivid demonstration of why human oversight remains indispensable in the age of generative AI.