Rivian's brand philosophy extends beyond functionality. The company deliberately includes design elements, like a built-in flashlight in the door, not just to serve a purpose, but to act as an 'invitation to explore.' This strategy aims to make the vehicle a catalyst for memorable experiences, inspiring customers to live the adventurous life the brand represents.
Despite just launching its first-generation autonomy system, Rivian completely reset it, throwing away all the code and hardware. CEO RJ Scaringe said the decision was easy because it was obvious that the old rules-based architecture had a 0% chance of being competitive against modern neural net-based approaches.
RJ Scaringe argues that successful, neural net-based autonomy requires a rare combination of ingredients: full control of the perception stack, a large vehicle fleet for data collection, massive capital, and GPU access. He believes only a handful of companies, including Rivian, Tesla, and Waymo, possess all the necessary components to compete.
While public focus is often on expensive sensors like LiDAR, Rivian's CEO states the onboard compute for AI inference is an order of magnitude more expensive than the entire perception stack. This cost reality drove Rivian to design its own chip in-house, enabling it to deploy high-level autonomy capabilities across all its vehicles affordably.
Rivian's CEO argues that the EV adoption rate in the US is not a reflection of consumer disinterest, but a direct result of a lack of product variety. With most non-Tesla EVs mimicking the Model Y's form factor, consumers who self-identify with their vehicles have few compelling alternatives, stalling mass-market conversion from internal combustion engines.
Traditional cars use a domain-based architecture with up to 150 separate control units (ECUs) from different suppliers, making software updates nearly impossible. This fragmented system, which evolved haphazardly from early fuel-injection computers, is a primary barrier for legacy automakers trying to compete with the software-defined, OTA-updatable vehicles from companies like Rivian.
While large language models (LLMs) converge by training on the same public internet data, autonomous driving models will remain distinct. Each company must build its own proprietary dataset from its unique sensor stack and vehicle fleet. This lack of a shared data foundation means different automakers' AI driving behaviors and capabilities will likely diverge over time.