Why Apple Just Gave Up on AI
TL;DR
Apple is paying Google $1 billion annually to power Siri with a custom Gemini model after years of embarrassing delays and internal dysfunction, raising serious questions about whether massive investments in proprietary AI infrastructure are necessary when companies can simply lease commoditized large language models.
🏢 Apple's Internal AI Crisis 3 insights
Siri delays called ugly and embarrassing
Senior director Robbie Walker called the delays an absolute disaster after promised iPhone 16 features failed to materialize despite heavy advertising.
Leadership exodus and internal blame
Head of AI Ruming Pang departed for Meta in June 2025 as internal teams fractured and blamed each other for announcing vaporware features.
New Siri pushed to 2026
The revamped Siri has been pushed to Q2 2026, leaving current versions unable to execute in-app actions without passing queries to ChatGPT.
🤝 The Google Gemini Partnership 3 insights
$1 billion annual deal for custom AI
Apple will pay $1 billion annually for a custom 1.2 trillion parameter Gemini model, significantly larger than its internal 150 billion parameter system.
Privacy-preserving cloud arrangement
The special Gemini version runs exclusively on Apple's private cloud servers to preserve the company's privacy-first brand positioning.
Admission of strategic defeat
This partnership marks a rare strategic concession for Apple, which typically develops core technologies internally rather than outsourcing to rivals.
📉 Consumer Reality Check 3 insights
Only 11% upgrade for AI features
Only 11% of US smartphone users upgraded for AI features, down 7% year-over-year, with 30% finding mobile AI actively unhelpful according to CNET.
Samsung's Galaxy AI disappointment
Samsung cited only market weakness in earnings calls despite heavy Galaxy AI marketing, confirming AI features failed to drive expected upgrades.
Nice-to-have versus must-have
AI capabilities rank far below price, battery life, and cameras in consumer purchase decisions, serving as optional perks rather than upgrade drivers.
💰 Industry Implications 3 insights
Questioning trillion-dollar infrastructure bets
Apple's pivot challenges the efficiency of massive AI infrastructure spending when companies can license existing models rather than building from scratch.
LLMs as future commodities
Large language models risk becoming commoditized utilities, making application layers like Siri more valuable than owning the underlying base models.
Samsung's existing precedent
Samsung already licenses Gemini for Galaxy AI, suggesting third-party AI partnerships may become standard industry practice rather than proprietary development.
Bottom Line
Businesses should prioritize building applications on licensed AI models rather than burning capital on proprietary foundational models that consumers barely use and may soon become interchangeable commodities.
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