Apple reveals new AI architecture built around Google Gemini models

Original Article ↗ Hacker News Discussion ↗

Architecture & Model Choices

  • Apple described five “Apple Foundation Models” (AFMs): two on‑device (Core, Core Advanced) and three cloud (Cloud, Cloud Image, Cloud Pro).
  • Cloud Pro is said to be “Gemini frontier‑level” and runs on NVIDIA GPUs in Google Cloud under Apple’s Private Cloud Compute (PCC); others run on Apple Silicon.
  • Everything except Cloud Pro is described as custom Apple models “refined” using Gemini; commenters speculate this means some form of distillation or fine‑tuning, but details are unclear.
  • Some links claim earlier on‑device AFM was ~3B parameters; people note the new stack is “more complicated” than that.

Gemini Quality & Hallucinations

  • Several posters complain that public Gemini (especially search “AI mode”) is inaccurate and hallucinatory; some prefer Claude or ChatGPT.
  • Others distinguish Google Search’s AI mode from the Gemini app/API, saying the latter is much better, especially paid tiers (e.g., Ultra).
  • Local Gemma‑based models are praised on phones, but some report current iOS local Gemini is slow, hot, and still hallucinates.

Privacy, Private Cloud Compute, and Trust

  • Apple claims: on‑device first, PCC for offload, data only used per request, and not accessible to Apple or third parties; security researchers can verify via published PCC design.
  • PCC uses confidential computing (Apple Silicon, and now also Intel/NVIDIA on Google Cloud) plus OHTTP‑style relays; keys are held so operators allegedly can’t inspect data.
  • Supporters call this the best available privacy architecture for off‑device inference.
  • Skeptics point out: users must still trust Apple (and underlying hardware vendors, and jurisdiction); nation‑state backdoors, zero‑days, or legal compulsion remain possible; some call it “security theater” unless independently and continuously audited.

EU DMA, Regulation, and Feature Delay

  • Siri AI / Apple Intelligence is delayed in the EU. Apple blames the DMA’s requirement for parity of access to device data and actions for third‑party assistants.
  • One side: giving “any AI app” Siri‑level permissions (read all personal data, control apps) is too risky; Apple is right to resist.
  • Other side: this is about preserving lock‑in and avoiding competition; users should be allowed to choose other assistants with explicit permissions, as with contacts/photos today.
  • Debate over paternalism vs user autonomy; some argue safeguards and system prompts would suffice, others think average users will be tricked by Meta‑style dark patterns.

Why Google (and Not Others)?

  • Reasons discussed:
    • Google’s strength in small/edge models (Gemma, on‑device Gemini) and prior edge‑AI work.
    • Massive compute capacity (TPUs, GPUs, data centers) and willingness to host Apple’s own AFMs under PCC.
    • Existing multibillion‑dollar search partnership and perceived corporate stability vs newer labs.
  • Many think models are becoming commodities; the real differentiation will be Apple’s integration, orchestration, and UX.

User Choice, Lock‑in, and Third‑Party Assistants

  • Some want a system‑level way to plug in alternative models (Claude, Mistral, DeepSeek, self‑hosted) behind the same Apple Intelligence APIs.
  • Others argue Apple won’t (and isn’t required to) operate everyone’s models in PCC, and that open routing to arbitrary clouds would weaken privacy guarantees.
  • DMA debate recurs here: whether “same access as Siri” should apply only to on‑device APIs, or also to cloud backends.

Siri, UX, and App Integration

  • Many say legacy Siri was “terrible” and are skeptical that reusing the Siri brand will change perception.
  • Technical talks about App Intents and Shortcuts suggest a deep agentic layer: AI can coordinate across apps, change passwords, book travel, etc.
  • Some are excited about OS‑level integration that third‑party chatbots can’t match; others fear brittle automation (e.g., AI mis‑changing passwords) and complex failure modes.

Views on Apple’s Overall AI Strategy

  • Critique: Apple is “weirdly behind,” relying on others for core AI, showing loss of innovation leadership and over‑focus on operations.
  • Counter‑view: Apple wisely avoided burning tens of billions on training frontier models; now it can rent or co‑develop models once the landscape is clearer.
  • Several note Apple has long positioned itself as a hardware+integration company, not a search/AI lab; treating the model as a swappable implementation detail fits that philosophy.