⚡️Every product of the future will be a living system — Ronak Malde, Trajectory.ai
Ronak Malde, co-founder of Trajectory.ai, discusses his journey from Windsurf to DeepMind and ultimately to building Trajectory.ai. The conversation centers on the critical need for continual learning in AI products, moving beyond static models to systems that constantly adapt from real-world user interactions. Malde shares insights into their platform, technical innovations like Self-Distillation Policy Optimization and Continuous LoRA, and the ambitious vision to make every product a self-learning 'living system'.
- Static AI is a Dead End: Current AI models, despite their power, act like traditional software—unchanging. They repeat mistakes because they don't learn from real-world user corrections or interactions, wasting valuable feedback. Read →
- Traditional Reinforcement Learning (RL) crumbles in continual learning scenarios because it compresses rich, real-world user interactions into a single, insufficient reward number. Read →
- For highly regulated fields like legal, achieving 80% AI accuracy offers zero real utility. The true value comes from capturing specific human corrections. Read →