AI Should Evolve Through Self-Written Code, Not Just Model Scaling: YC's Diana Hu

According to Y Combinator partner Diana Hu on X, the future of AI development lies in building thin software layers on top of foundation models that enable AI systems to write and refine code autonomously, rather than solely expanding model parameters. The approach allows AI to test, modify, and simplify code based on execution results without requiring expensive fine-tuning of the base model itself.

Hu's perspective echoes recent research by Wen Jiayue, a core member of OpenAI's post-training team, who demonstrated that large models can master tasks by writing Python code and debugging without adjusting any model parameters—exemplified by successfully training on Atari game performance.