Stanford's conclusion is quite sobering: opening weights is just the starting point; data barriers are the real moat.

Stanford NLP team on Twitter stated that the publicly available agent training data is mainly used for the fine-tuning stage, especially for models like Qwen. These models may have already been trained on large amounts of agent data. They believe that the amount of agent data needed to train excellent open-source models from scratch far exceeds the scale of fine-tuning solely based on open weights, highlighting the insufficiency of agent data during the pretraining phase. Source: InFoQ