Apple plans to release an upgraded version of its Siri voice assistant by spring 2026 as part of the iOS 26.4 software update, according to sources familiar with the situation.

This update will include features that allow Siri to access personal data and on-screen activities for more accurate responses, although it has faced several delays.

The target release timeframe aligns with Apple’s history of launching “.4” updates around March.

However, the exact date remains uncertain, and Apple has not provided specific timing for these new features.

🔗 Source: Bloomberg

🧠 Food for thought

1️⃣ Siri’s technical debt has deep historical roots dating back to 2011

The current Siri delays aren’t just a recent challenge but reflect longstanding architectural problems that Apple has struggled with for over a decade.

Back in 2018, Apple engineers found that Siri’s original infrastructure was so inefficient that a significant code rework reduced server demands from 500 to just 5 servers, demonstrating severe technical debt in the system1.

The assistant’s code was described as “brittle” and “inflexible,” making it difficult to introduce new features even years ago, a problem that persists in today’s development challenges1.

According to internal reports, Apple’s current technical issues stem from trying to merge two different Siri architectures, one for basic tasks and another for newer features, causing bugs and necessitating a complete rebuild2.

This pattern of architectural challenges helps explain why Apple has repeatedly missed deadlines for the Siri upgrade, shifting from fall 2024 to spring 2025, then to May 2025, and now to spring 2026.

2️⃣ Apple’s privacy-first approach creates an AI development paradox

Apple faces a fundamental tension between its privacy-focused business model and the data-hungry nature of modern AI development.

The company employs sophisticated techniques like federated learning and differential privacy to train Siri without collecting raw user data, an approach that preserves privacy but potentially slows development compared to competitors2.

This methodical approach stands in contrast to companies like OpenAI and Google, which have access to vast amounts of user data for training their models, potentially giving them an advantage in development speed and capability3.

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