Frame AI as a fundamental productivity shift, like the personal computer, that will achieve total market saturation. It's not a speculative bubble but a new, permanent layer of the economy that will be integrated into every business, even a local taco truck.

The vast network of consumer devices represents a massive, underutilized compute resource. Companies like Apple and Tesla can leverage these devices for AI workloads when they're idle, creating a virtual cloud where users have already paid for the hardware (CapEx).

Previous technology shifts like mobile or client-server were often pushed by technologists onto a hesitant market. In contrast, the current AI trend is being pulled by customers who are actively demanding AI features in their products, creating unprecedented pressure on companies to integrate them quickly.

Apple's seemingly slow AI progress is likely a strategic bet that today's powerful cloud-based models will become efficient enough to run locally on devices within 12 months. This would allow them to offer powerful AI with superior privacy, potentially leapfrogging competitors.

The current focus on building massive, centralized AI training clusters represents the 'mainframe' era of AI. The next three years will see a shift toward a distributed model, similar to computing's move from mainframes to PCs. This involves pushing smaller, efficient inference models out to a wide array of devices.

The prohibitive cost of building physical AI is collapsing. Affordable, powerful GPUs and application-specific integrated circuits (ASICs) are enabling consumers and hobbyists to create sophisticated, task-specific robots at home, moving AI out of the cloud and into tangible, customizable consumer electronics.

The PC revolution was sparked by thousands of hobbyists experimenting with cheap microprocessors in garages. True innovation waves are distributed and permissionless. Today's AI, dominated by expensive, proprietary models from large incumbents, may stifle this crucial experimentation phase, limiting its revolutionary potential.

The future of AI isn't just in the cloud. Personal devices, like Apple's future Macs, will run sophisticated LLMs locally. This enables hyper-personalized, private AI that can index and interact with your local files, photos, and emails without sending sensitive data to third-party servers, fundamentally changing the user experience.

The narrative of endless demand for NVIDIA's high-end GPUs is flawed. It will be cracked by two forces: the shift of AI inference to on-device flash memory, reducing cloud reliance, and Google's ability to give away its increasingly powerful Gemini AI for free, undercutting the revenue models that fuel GPU demand.