Generating a condor and an anaconda together is an exercise in high dimensional interpolation yes but that doesn't equate to conceptual understanding.

The model has seen millions of examples of both animals and millions of images of water. It can blend these known patterns because they already exist in high density areas of the training data. This does not require an understanding of biology or physics.

It only requires the model to align textures and edges in a way that satisfies the statistical likelihood of those pixels appearing together. The model is a probability engine predicting the most likely pixel values based on its training set. It makes the appearance of physics without any internal model of fluid dynamics. If you asked it to depict a physical interaction that hasn't been photographed millions of times, the "understanding" would immediately vanish.

When a model successfully generates an Andean condor fighting an anaconda in a splashing river, it is interpolating between highly distant regions of its latent space to synthesize a completely out of distribution intersection. It is taking isolated distributions (condor, snake, water physics, camera lens) and generating a novel zero shot combination. That is the literal definition of generalization.

Claiming that a condor fighting an anaconda is "out-of-distribution" ignores how dense the training data is for those specific elements. Both animals, water splashes, and National Geographic aesthetics occupy massive, high-density regions of the model's latent space. Combining them is a standard case of interpolation; the model is simply blending known statistical patterns rather than inventing a new physical logic. This is a far cry from generalization.

Eridian from Project Hail Mary is a much more rigorous test of structural logic. This species requires a pentameric symmetry that is extremely rare in the training set compared to bilateral symmetry.

To render it correctly, a model would need to maintain a specific geometric constraint across a novel form. If the model simply generates a generic alien with limbs in random places, it is failing to follow the internal logic of the prompt. It shows the difference between something weird and something that actually follows a coherent system.

A high-quality 2D rendering of a bird does not prove the model knows how a skeletal system works. It only proves the model is excellent at mapping the pixel texture of feathers to a specific shape.

A text to image model is a visual synthesizer, not a biological simulation engine.

Humans are neither but they have true generalization.