Apple's ability to distill Google's large Gemini models into smaller, proprietary versions reveals a strategy to accelerate its own on-device AI development, not just rely on Google's tech. This gives Apple a 'cheat code' to catch up quickly and power its core vision for local AI on iPhones.

While competitors spend billions on centralized data centers, Apple's powerful, memory-rich Mac hardware has become the go-to for developers running local AI models. This positions Apple as a key, decentralized infrastructure provider by accident, a powerful market position they have yet to officially capitalize on.

Apple's inability to ship its own cutting-edge AI model has paradoxically become a strategic advantage. Instead of bearing the immense cost of foundation model development, they can now integrate best-in-class third-party models onto their dominant hardware ecosystem, a position Mark Gurman calls 'falling ass backwards into it.'

The Neural Engine, the specialized AI chip in iPhones, was a direct result of the canceled Apple Car project. It was designed to power a self-driving car's AI and was later shrunk for the phone. Without the car project, Apple would be even further behind in on-device AI.

Apple crushed competitors by creating its M-series chips, which delivered superior performance through tight integration with its software. Tesla is following this playbook by designing its own AI chips, enabling a cohesive and hyper-efficient system for its cars and robots.

While competitors spend billions on data centers, Apple's focus on powerful on-device chips cleverly offloads the enormous cost of AI compute directly to consumers. Customers pay a premium for new devices capable of local inference, creating a massively profitable and defensible AI business model for Apple.

Prism ML claims it can shrink massive AI models to run on an iPhone without performance loss, a feat Apple has struggled with. Apple's own attempts resulted in drastically decreased accuracy, making Prism ML's technology a high-value solution and a potential acquisition target for Apple's on-device AI ambitions.

While critics viewed Apple's lack of AI investment as a failure, it resulted in a strong strategic position. By waiting out the initial model development race, Apple avoided massive R&D costs and can now partner with leading model providers to integrate AI into its dominant hardware ecosystem.

The abandoned Apple Car project, despite being a failure, had a critical strategic benefit: it spurred the development of the Neural Engine. Originally conceived to power a self-driving car's AI, the chip was adapted and integrated into the iPhone, giving Apple a foundational piece of AI hardware it would have otherwise lacked.