The current voice-only Siri interface is ephemeral. For deep research or multi-step tasks powered by LLMs like Gemini, users need a persistent, scrollable chat history, similar to texting a friend, to pick up where they left off.
Currently, Apple pays Google for search defaults. The hosts predict this will reverse for AI. As inference costs drop and monetization (via ads, affiliate fees, transactions) improves, LLM queries will become profitable on average, making access to Apple's users a revenue stream worth paying for.
OpenAI's move into healthcare is not just about applying LLMs to medicine. By acquiring Torch, it is tackling the core problem of fragmented health data. Torch was built as a "context engine" to unify scattered records, creating the comprehensive dataset needed for AI to provide meaningful health insights.
The hosts argue that a key test for agentic AI on iOS is its ability to perform OS-level tasks with a single prompt, like automatically organizing a messy desktop of apps into logical folders. This trivial-seeming task demonstrates deep OS integration and is a practical benchmark for "Apple Intelligence."
Instead of an exclusive AI partner, Apple could offer a choice of AI agents (OpenAI, Anthropic, etc.) on setup, similar to the EU's browser choice screen. This would create a competitive marketplace for AI assistants on billions of devices, driving significant investment and innovation across the industry.
Leaks suggest OpenAI's first hardware device will be an audio wearable similar to AirPods. By choosing a form factor with proven product-market fit and a massive existing market ($20B+ for Apple), OpenAI is strategically de-risking its hardware entry and aiming for mass adoption from day one.