AI For AI
178: 与田渊栋聊 RSI：模型自进化如何到来？ adds [[TianYuandong|田渊栋]]’s [[Recursive|Recursive Superintelligence]] version. Here AI for AI means using models to discover better model designs, training logic, optimization methods, and research workflows, with AI Research Feedback Compression as the near-term mechanism and Recursive Self-Improvement as the harder target.
AI for AI is the source’s umbrella for using AI to improve AI research, model design, training, and experimentation. In 149. 亲历中美 New Labs 资本狂潮，和清华刘子鸣聊：AI for AI、机制可解释性和 Max Tegmark, [[LiuZiming|Liu Ziming]] treats it as the natural successor to AI for coding, but argues that research is much less structured than code because there is no GitHub-like corpus of complete experiment reasoning.
Liu’s version differs from a simple paper agent or coding agent. He wants Physics Of AI first, then AI that can automate that physics-like understanding, and only then AI that improves AI. The source condenses this as “AI for Physics of AI for AI.”
「热爱一个行业15年的理由是什么？」｜对谈汪天凡：我要投真正的快乐、投最纯的愿景、投人性的光辉【公路播客】 adds Will Wang Tianfan / 汪天凡’s investment framing. He names using models to develop models as one of three AI-company growth drivers, alongside data flywheels and [[AIFirstOrganization|AI-first organizations]] that use internal context to reduce coordination friction.
Key Claims
- AI for AI can include coding, paper reading, hypothesis generation, experiment execution, failure analysis, and model-design search.
- The hard part is not only trying more ideas; it is raising the quality of idea selection and failure interpretation.
- Research-process structure matters because unpublished thoughts, failed turns, and “aha moments” are usually missing from papers.
- OPHIS Research Workflow is Liu’s proposed first step for making research legible enough to train on.
- Meta-Model Training Curve Prediction can reduce compute waste by predicting which architecture ideas deserve real training.
- Training Autopilot and Vibe Training are productized forms of AI for AI, but the source treats them as future horizons rather than solved products.
- AI for AI may be necessary for AGI in Liu’s view, but it is not sufficient without stronger abstraction and continual learning.
- The Wang Tianfan source treats AI for AI less as a research architecture and more as a growth driver that can compound a model or AI-native company.
- Tian’s source adds that AI for AI should be judged by whether it raises research quality, not only by whether it runs more coding tasks or benchmark searches.
Connections
- Auto Research — operational research-automation loop that overlaps with AI for AI.
- Recursive Self-Improvement — stronger self-improvement loop that AI for AI may eventually feed.
- Recursive — compared in the source as a more coding-agent or “diligent” route.
- Physics Of AI, Mechanistic Interpretability, and AI Interpretability By AI — model-understanding path Liu wants to automate.
- OPHIS Research Workflow, Meta-Model Training Curve Prediction, Training Autopilot, and Vibe Training — source-specific workflow and product path.
- Research Taste, AI Verification, and Training Compute Allocation — bottlenecks for making automated research useful.
- Will Wang Tianfan / 汪天凡, AI Data Flywheel / AI数据飞轮, and AI-First Organization — investment-growth-driver branch added by the Wang Tianfan source.
- Tian Yuandong / 田渊栋, Recursive, AI Research Feedback Compression, Frontier Model Scaling, and Mechanistic Interpretability — founder/operator branch added by LateTalk episode 178.