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.

Incumbent automakers evolved with 100+ separate computer modules, creating a complex system. Newcomers like Rivian and Tesla start with a centralized, "zonal" architecture. This clean-sheet design dramatically simplifies over-the-air updates, reduces costs, and enables more advanced, integrated AI features.

Rivian's CEO explains that early autonomous systems, which were based on rigid rules-based "planners," have been superseded by end-to-end AI.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.

Rivian's CEO argues that foregoing CarPlay allows for a more seamless, AI-driven experience where the car's OS has full knowledge of vehicle state. This is a strategic bet on creating a superior, proprietary ecosystem over offering third-party integration.

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.

While Over-the-Air (OTA) updates seem to make hardware software flexible, the initial OS version that enables those updates is unchangeable once flashed onto units at the factory. This creates an early, critical point of commitment for any features included in that first boot-up experience.

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.

Large enterprises operate on complex webs of legacy systems, compliance controls, and fragile integrations. Their high risk aversion and lengthy change management cycles create a powerful inertia that will significantly delay the replacement of established B2B software, regardless of how capable AI agents become. Enterprise architecture moves slower than market hype.

GM's next-generation platform, debuting in 2028, centralizes all vehicle compute and uses Ethernet networking. This isn't just about more processing power; it enables sub-millisecond response times for dynamic systems like suspension, a 10x improvement. This architecture abstracts hardware from software, allowing for much faster and more comprehensive over-the-air updates.