Apple squandered the Holy Grail

Foundations vs. Execution of Apple Intelligence

• Many commenters think Apple built a strong technical and privacy foundation (on‑device models, Private Cloud Compute, hardware NPUs), but shipped weak, fragmented user-facing features.
• Some argue Apple is following its usual pattern: v1 is mediocre but the long-term architecture is sound, so future iterations may become compelling (similar to Maps or early iPhone).
• Others counter that this launch is unusually poor “for Apple”: delayed, underdelivering vs WWDC demos, and often just bad or unfinished.

Privacy, Data, and Trusted Compute

• Debate on whether Apple’s privacy stance conflicts with LLMs:
  • One side: LLMs largely train on public data; Apple’s privacy promises mostly concern private user data, so no fundamental clash.
  • Other side: even “public” posts (Reddit, papers) shouldn’t automatically be reused for training; Apple’s rhetoric encourages stronger user control.
• Private Cloud Compute is viewed as ambitious and close to a “holy grail” of trusted remote inference. Some are skeptical such guarantees can truly be met; others think it’s the right architecture for regulation and trust.

Feature Quality and UX

• Math Notes is widely discussed:
  • Some see it as great—algebraic text/handwriting calculations as an everyday “bicycle for the mind.”
  • Others say it doesn’t need LLMs at all; similar functionality existed in tools like Soulver, Calca, Wolfram Alpha, OneNote, etc.
• Notification summaries, mail categorization, and image playground are frequently criticized as inaccurate, uncanny, or “AI slop.” Some enjoy summaries for quick triage and humor.
• Photo “Clean Up” sparks ethical and aesthetic worries about rewriting reality vs traditional editing; others see it as just another post-processing tool.

Comparisons to Competitors and Ecosystem

• Several note that no mobile AI assistant (Apple, Google, Microsoft, Amazon) is reliably good yet; Siri remains weak, but Assistant/Gemini and Alexa are also seen as degraded or ad-heavy.
• Some argue Apple was caught flat-footed by ChatGPT and is now rushing to have an “AI story” for investors; others think they were already deep into ML and mainly rebranding.
• There’s recurring criticism of Apple’s broader software quality (Siri, Maps in many regions, Music, Notes regressions) versus praise for hardware, M‑series chips, and some first‑party apps.

AI’s Broader Value

• Strong split:
  • Some assert current LLMs are overblown, a local minimum or mere hype.
  • Others report large productivity and domain-specific gains (coding, research, media production, tutoring), arguing that dismissing AI as useless is detached from real-world benefit.