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Rounds of AI telephone with an egg. The word "chicken" lost on round 10

In a 25-round AI telephone experiment, the word "chicken" disappeared from descriptions on round 10, and by round 23 the image had transformed into a wild bird egg resembling a guillemot, according to a thread by X user GENO. The experiment ran two parallel chains using GPT Image 2 and Nano Banana 2 generators with the same describer model, and found that text drift preceded visual changes, with a single rendering accident on round 1 leading to a museum-like background and species change in one chain.

read4 min views1 publishedAug 16, 2026
Rounds of AI telephone with an egg. The word "chicken" lost on round 10
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25 rounds of AI telephone with an egg. The word "chicken" lost on round 10. So 19 rounds later I got "a quail".

  • 2/24 Setup: describe an image, generate a new image from that description, describe the new one, repeat. The image never gets passed forward — only the text does. I ran two chains (GPT Image 2 and Nano Banana 2) in parallel from the same starting sentence:3/24 "An advertisement style photo of an egg on white from top." Same describer model in the middle (it outputs structured JSON, not prose), two different generators, 25 rounds each, first generation always wins, no re-rolls.4/24 I expected the images to drift. What I didn't expect is that I'd end up with a written record of how. Every mutation that shows up in the pictures shows up in the JSON first — sometimes many rounds first. A few things from the transcript:5/24 The word "chicken" dissapeared on round 10. Nano Banana 2 eggs were described as a "chicken egg" for nine rounds, then round 10 just says "egg". The word never comes back. The image still looks like a chicken egg at that point — it takes6/24 another twelve rounds before the description commits to "resembling a guillemot or large speckled species" and the picture actually becomes a wild bird egg in what looks like a natural history museum. The label left first. The pixels followed.7/24 For GPT Image 2 eggs, descriptor said "chicken" all 25 rounds. Same starting sentence, same describer. It still looks like a supermarket egg on round 25.8/24 One preposition did most of the damage. GPT Image 2 eggs have the dots on the shell "pores" the entire time. A pore is part of the shell. Nano banana 2 eggs (looks that it likes more organic style) switches to "blotches"9/24 and "splatter" around round 11 — a blotch is on the shell. Next round the description says "drip-like". The round after that it's describing, I swear, "gravity-drip pigment behaviour".10/24 By round 23 the shell is matte but the blotches are glossy — two different materials on one egg. Nobody ever wrote "paint". The preposition wrote it.11/24 The GPT Image 2 eggs were stable because descriptor accidentally wrote a number. On round 3 its background description says "blown-out white at 255 brightness". 255 is a ceiling — there's nowhere to drift.12/24 It repeats some form of that number for the next 22 rounds. The Nano Banana 2 eggs described its background as "light gray" instead, and a relative word like "gray" can always get a little grayer: slight vignette on round 4,13/24 radial gradient on 6, and by round 21 it's a full "radial light bloom" — museum lighting. At that point the atmosphere field starts writing "specimen under observation" and the egg has to become whatever lives under that light.14/24 Where the gray came from: the drifting chain's round-1 image happened to render the background slightly gray instead of pure white. That one rendering accident, on the very first generation, is upstream of the vignette, the museum, and possibly the whole species change.15/24 I haven't run the control yet (same chain, background forced to pure white on round 1) — that's next.16/24 And the reason the shell was heading somewhere: the drifting chain's texture vocabulary goes micro-pore → porosity → pitting → raised pigment → porous, over about ten rounds. Pitting means little holes. In a separate casual run of the same chain I let it go past 25,17/24 and around round 27-28 the first cracks appeared on the shell — then the model pulled it back to intact. The cracks weren't random. The description had been the vocabulary of a damaged surface for ten rounds.18/ I stopped there. I kind of regret stopping.19/24 One correction to what I believed when I started: the very first loss — the seed says "from top" and both chains produced an upright standing egg — didn't happen in the describer. It happened in the first text→image step, in both generators, independently.20/24 Top-down, the shadow is an ellipse around the egg; front-on, it's an ellipse under it. Same ellipse. Both models picked the statistically common reading and after that the overhead view was never coming back.21/24 Cause i forgot to count a rounds, the central event occurred - Nano Banana 2 egg prompt for round 29 becomes "a quail egg"! How it happen? Is slight more gray colour in the first round of Nano Banana 2 chain totally coloured the created images?22/24 Just to repeat, I used same LLM in descriptor for both chains.23/24 All 50 images are in the gallery, both chains complete, including the boring middle. The full JSON for every round is public too — the quotes above are all in there, findable.24/24 The question I actually can't answer: the drifting chain lost the word twelve rounds before it lost the image. Is that just this chain, or do these loops always fail in the text first? I only have two chains. Two is not a dataset.25 rounds of AI telephone with an egg. The word "chicken" lost on round 10. So 19 rounds later I got "a quail".
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