Rivian's CEO explains that early autonomous systems, which were based on rigid rules-based "planners," have been superseded by end-to-end AI. This new approach uses a large "foundation model for driving" that can improve continuously with more data, breaking through the performance plateau of the older method.
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The shift to AI makes multi-sensor arrays (including LiDAR) more valuable. Unlike older rules-based systems where data fusion was complex, AI models benefit directly from more diverse input data. This improves the training of the core driving model, making a multi-sensor approach with increasingly cheap LiDAR more beneficial.
Rivian's decision to forgo CarPlay is a long-term strategic bet on AI. The company believes that to deliver advanced, integrated AI features, it must control the entire digital experience, connecting vehicle state, driver history, and various apps—a task it argues is impossible when ceding control to an overlay like CarPlay.
The neural nets powering autonomous vehicles are highly generalizable, with 80-90% of the underlying software being directly applicable to other verticals like trucking. A company's long-term value lies in its scaled driving data and core AI competency, not its initial target market.
The latest Full Self-Driving version likely eliminates traditional `if-then` coding for a pure neural network. This leap in performance comes at the cost of human auditability, as no one can truly understand *how* the AI makes its life-or-death decisions, marking a profound shift in software.
Autonomous systems can perceive and react to dangers beyond human capability. The example of a Cybertruck autonomously accelerating to lessen the impact of a potential high-speed rear-end collision—a car the human driver didn't even see—showcases a level of predictive safety that humans cannot replicate, moving beyond simple accident avoidance.
The evolution of Tesla's Full Self-Driving offers a clear parallel for enterprise AI adoption. Initially, human oversight and frequent "disengagements" (interventions) will be necessary. As AI agents learn, the rate of disengagement will drop, signaling a shift from a co-pilot tool to a fully autonomous worker in specific professional domains.
To achieve scalable autonomy, Flywheel AI avoids expensive, site-specific setups. Instead, they offer a valuable teleoperation service today. This service allows them to profitably collect the vast, diverse datasets required to train a generalizable autonomous system, mirroring Tesla's data collection strategy.
Despite rapid software advances like deep learning, the deployment of self-driving cars was a 20-year process because it had to integrate with the mature automotive industry's supply chains, infrastructure, and business models. This serves as a reminder that AI's real-world impact is often constrained by the readiness of the sectors it aims to disrupt.
A human driver's lesson from a mistake is isolated. In contrast, when one self-driving car makes an error and learns, the correction is instantly propagated to all other cars in the network. This collective learning creates an exponential improvement curve that individual humans cannot match.
Unlike older robots requiring precise maps and trajectory calculations, new robots use internet-scale common sense and learn motion by mimicking humans or simulations. This combination has “wiped the slate clean” for what is possible in the field.
