Part 4/10:
James points out that Tesla's use of scaling laws echoes how OpenAI approached GPT-4's development. OpenAI’s meticulous evaluation of scaling behaviors enabled a massive investment with a high confidence of payoff. For Tesla, similarly, understanding these relationships directly impacts their R&D efficiency—Tesla spends significantly less on research compared to traditional automakers like Toyota, thanks to their strategic use of data and scaling.
This efficiency stems from Tesla’s enormous real-world data collection. With over 300 million miles driven using FSD Beta and aiming for billions more, Tesla's data advantage is unmatched. Such extensive data collection allows Tesla to improve neural network training, making their AI systems more robust and reliable over time.