Core Viewpoint - Tesla is shifting its focus towards synthetic data for training its Full Self-Driving (FSD) models, moving away from reliance on real-world data, which enhances efficiency, cost-effectiveness, and data coverage [5][6][7]. Group 1: AI Chip Development - Tesla's AI5 chip is expected to be the best for models with parameters below approximately 250 billion, boasting the lowest silicon cost and the highest performance-to-power ratio [1]. - The upcoming AI6 chip is anticipated to surpass AI5 in capabilities, consolidating the design efforts of Tesla's chip team [1]. - The transition to a single chip architecture allows Tesla's silicon talent to focus on creating an exceptional chip [1]. Group 2: Data Generation and Model Training - The traditional FSD model training process involved collecting real-world data, while the new approach utilizes a powerful cloud-based world model to generate synthetic data through inference [6][7]. - The inference process in Tesla's world model directly produces training materials, creating a feedback loop where the model's capabilities and data scale mutually enhance each other [8][10]. - The new training process relies on synthetic data generated from the world model's inference, marking a shift from traditional methods that depended solely on real-world data [9][10]. Group 3: Future Directions - In the next 2-3 years, Tesla aims to train a large-scale world model using NVIDIA GPU clusters, followed by using AI5 and AI6 chips in a Dojo 3 system for inference to generate synthetic data [6][7]. - The strategy involves a mixed data approach, where real-world data remains important but is supplemented by synthetic data to accelerate iteration and improve model performance [7][10]. - The closed-loop ecosystem created by this approach allows for continuous improvement of both the world model and the FSD model, enhancing their capabilities over time [10].