The Next Frontier of Visual AI Is Code

Over the past few years, visual AI has been judged by the pixel quality of the images or videos it generates. Diffusion models have produced beautiful images from text, but they have been limited in providing editable elements such as layers, components, and keyframes that designers or animators need.

Recently spotlighted tools focus on generating the source code behind the final output, rather than directly creating the pixels themselves. This is a shift that redefines visual generation work not as simple image creation, but as sophisticated coding work.

Visual generation methods are broadly divided into two stacks.

• Pixel-native generation: Generates images directly in latent space, with strengths in texture and realistic depiction.
• Code-native generation: Generates executable programs such as SVG, HTML/CSS, React components, and Blender scripts.

Code-based generation allows the output to function not just as a simple result, but as an artifact that can be modified and reused. Users can directly edit paths, gradients, layouts, and more through the generated code, which can be immediately integrated into professional design and development workflows.