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.
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In contrast to the 'move fast' ethos of tech rivals, GM views its intense focus on safety as a core business strategy. The company believes that building and retaining customer trust is paramount for new technologies like autonomous driving. It sees a single major incident as catastrophic to public perception, making a slower, safer rollout a long-term competitive advantage.
GM operates on a functional model, not siloed brand divisions, to maximize economies of scale. By developing a single core platform that can be adapted for different brands like Chevrolet and Cadillac, the company leverages its global scale to offer more features and technology at competitive price points, a key advantage in the capital-intensive auto industry.
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.
GM's new robotics division is leveraging a non-obvious asset: its vast, meticulously structured manufacturing data. Detailed CAD models, material properties, and step-by-step assembly instructions for every vehicle provide a unique and proprietary dataset for training highly competent 'embodied AI' systems, creating a significant competitive moat in industrial automation.
When building data platforms for industries with legacy hardware like automotive, the real work is data normalization. Different product lines use inconsistent signal names and units (e.g., speed as MPH vs. radians/sec), requiring a complex 'decoder' layer to create usable, standardized data.
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.
The evolution from simple voice assistants to 'omni intelligence' marks a critical shift where AI not only understands commands but can also take direct action through connected software and hardware. This capability, seen in new smart home and automotive applications, will embed intelligent automation into our physical environments.
GM's Chief Product Officer frames the controversial decision to ditch Apple CarPlay as a 'Jobsian' move, akin to removing the disk drive. The company believes its integrated, native infotainment system represents the next, superior technology 'S-curve' that will ultimately provide a better user experience by leveraging the car's unique hardware and capabilities.
Unlike competitors creating isolated 'skunkworks' teams for EV development, GM pursues a steady, integrated approach. The company believes this avoids the 'ingestion risk' of bringing a radical project back into the main organization, allowing innovations in battery tech and architecture to scale more quickly and efficiently across its massive global portfolio.
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.
