Apple finally admitted that Siri is outdated

Original Author: Sleepy

In the early hours of June 9, 2026, Beijing time, Apple's WWDC 2026 arrived as scheduled.

At the conference, it renamed Siri to Siri AI, announced a deep partnership with Google, using Gemini's model capabilities to train its own new generation of foundation models, and extended Private Cloud Compute for the first time to Google Cloud and Nvidia's GPUs.

It released five Apple Foundation Models, with the smallest on-device model having 3 billion parameters and the largest cloud model specifically optimized for Nvidia GPUs. Almost every everyday app was rewritten. Siri also got its own standalone app, capable of saving conversations, syncing across devices, and possessing memory.

This was Apple's most information-packed conference in years.

Taming a Future

Apple's AI story can be traced back to the fall of 2011, when the iPhone 4S was launched and Siri first took the stage.

At that time, Steve Jobs was gravely ill, and Apple stood at the crossroads of an era. Siri seemed like a little thing that had jumped out of a sci-fi movie. You asked about the weather, asked for restaurant recommendations, or told it to set an alarm, and it would reply in a slightly mechanical voice. For the first time, you felt your phone wasn't just a piece of cold glass.

Siri originated from SRI International's CALO project, which was originally a military-grade AI assistant funded by DARPA. Apple acquired it in 2010, reportedly for over $200 million according to TechCrunch. A year later, Siri debuted alongside the iPhone 4S, and Apple claimed it could understand natural language and perform tasks like a personal assistant.

At that moment, Apple possessed the world's best personal intelligence gateway. Then it squandered it for over a decade.

Looking back today, Siri's earliest impact was changing the way people interacted with machines. In 2011, the iPhone was transforming from a communication tool into a personal computing device. The App Store redefined software distribution, and the mobile internet migrated from PCs into the palm of your hand. Siri appeared at the peak of this upward trend. But once inside Apple, it quickly evolved from an ambitious personal assistant into an obedient voice remote control.

Apple inherently believes in closed systems and control. However, a true personal assistant must access more services, understand more context, and tolerate more uncertainty. Uncertainty means errors, means privacy risks, and means the disorder that Apple is least adept at handling.

Consequently, Siri was only allowed to perform deterministic tasks, like a tamed version of a once-promising future. It had a name, a voice, and a persona, but it lacked the initiative and memory necessary for a true personality. Users were initially amazed, then began to make jokes about it, and eventually stopped using it much at all.

Apple was the first to put a "personal assistant" into a phone, and it was also the first to lock it away.

Looking back at today's industry-wide focus on agents, Siri in 2011 was almost its prototype. One could say Apple was the first company to create an agent prototype, but it ended up being the last to finish the job.

AI That Doesn't Look Like AI

Did Apple's AI stagnate during the years Siri didn't grow up?

The answer is quite the opposite. Apple did a lot with AI, just in a way that didn't resemble typical AI.

If we go by media conference fanfare, it seems like Apple only started talking seriously about AI in 2024. But if we trace back along the technological path, Apple has been acting for the past decade.

In 2015, it acquired two companies in succession, one to supplement natural language conversation and one to explore running deep learning directly on the phone. The same year, WWDC introduced the Proactive Assistant, attempting to have the system make suggestions before the user even spoke. This idea was ahead of its time but felt more like a slogan under the technological conditions of the day.

The following year, it launched SiriKit, opening a limited window for developers to access Siri, and publicly discussed Differential Privacy, expressing its commitment to learning from large-scale data while protecting individual privacy. In 2017, the iPhone X introduced the Neural Engine. Face ID and the camera began relying on on-device machine learning. Apple also launched Core ML, allowing developers to run models on Apple devices, and acquired Workflow, which later became Shortcuts.

This was a very Apple-like set of answers. It wanted AI, but not like Google, which bet on the cloud and vast amounts of personal data. It wanted developers, but didn't want Siri to become a chaotic mess. So Apple chose the most difficult and slowest path: focusing on on-device processing, privacy, and system integration.

Around 2020, Apple bought several more companies specializing in low-power edge AI and voice understanding. That same year, the M1 chip was released, bringing the 16-core Neural Engine to the Mac, pushing on-device AI computing power from pocket-sized phones to computers. The next year, Live Text and Visual Look Up came to fruition, allowing text in photos to be copied directly and the camera to recognize plants and landmarks, while more voice requests were processed locally without leaving the device.

Over the past decade or so, Apple hasn't released a standalone AI app, but it has certainly made the phone smarter.

There was good reason for choosing this path. AI on a phone isn't just a question-answering machine; it needs to look at photos, listen to voice commands, understand contacts, invoke apps, and perceive battery life, location, and time. Ideally, it should be able to do some things even without an internet connection, rather than packaging up every piece of the user's life and uploading it to the cloud for every request. Apple's hardware control allows it to take this road.

But there is a deep chasm between localized intelligence and overall intelligence. Apple excels at breaking down technology into reliable components, but generative AI requires it to piece those components back together into a whole.

These components lay quietly embedded in the system, waiting for an opportunity.

But the opportunity didn't come first. ChatGPT came first.

When ChatGPT appeared in late 2022, Apple wasn't completely unprepared. Tim Cook repeatedly emphasized on multiple occasions that AI and machine learning were core technologies in Apple products for years. Bloomberg also reported in 2023 about Apple's internal Ajax large model framework and internal Chatbot project.

However, the problem wasn't whether Apple had cards to play; the problem was that the rules of the game had changed.

ChatGPT shifted user attention from "features" to "capabilities." Users started to assume their phones must have AI, and then compared who had the better one. When ChatGPT could already organize a jumble of thoughts into a coherent email, Siri was still saying, "Here’s what I found on the web."

At WWDC 2024, Apple unveiled Apple Intelligence. Writing tools, notification summaries, photo search, personalized Siri understanding, ChatGPT integration. Apple finally admitted that relying solely on its own models, at least in 2024, couldn't meet user expectations. But the promises it made ultimately failed to materialize according to the advertised timeline.

Hiring Google as a Tutor

Behind the delay of Apple Intelligence wasn't just the technology falling short; the structure of the entire Siri team couldn't keep up with this wave of AI.

Multiple media outlets confirmed that Apple's former AI head, John Giannandrea, stepped down, Craig Federighi took over AI direction, and Vision Pro head Mike Rockwell was brought in to lead the Siri team. A large number of Siri engineers were sent to learn AI programming tools. This wasn't a dignified rotation. Apple internally realized that with the original people and pace, they couldn't catch up.

In January 2026, Apple and Google issued a joint statement stating that Apple would leverage Gemini technology to customize Apple Intelligence features for the iPhone and other products. Reports suggest Apple plans to pay Google approximately $1 billion annually to use a customized 1.2 trillion-parameter Gemini model to power the Siri overhaul. Apple had also tested models from OpenAI and Anthropic but ultimately chose Google.

This was completely different from the ChatGPT integration in 2024. That time, ChatGPT was more like a backup called in by the user when Siri couldn't answer, with OpenAI's branding and a pop-up interface. This time, Gemini goes directly into the foundation, becoming part of Apple's new generation of foundation models.

The key action was distillation. Google gave Apple full access to Gemini. Apple used the large model in Google's data centers to generate high-quality answers and reasoning processes, then used these results to train smaller, cheaper models that could run on iPhones.

The day before WWDC, Apple published a technical paper packaging this collaboration as the third-generation Apple Foundation Models, developed in a customized collaboration with Google, resulting in five models. On-device models include AFM 3 Core with 3 billion parameters and AFM 3 Core Advanced, a sparse model with 20 billion parameters that only activates a portion per request. Cloud models include AFM 3 Cloud and image model ADM 3 Cloud, plus the most powerful AFM 3 Cloud Pro.

The more realistic change concerns computing power. No matter how intelligent the on-device models are, they can't complete all tasks. Apple's Private Cloud Compute infrastructure alone couldn't handle the full Gemini-level inference. Some requests would run on Nvidia GPUs in Google Cloud. Apple subsequently confirmed that PCC was extended for the first time beyond Apple's own data centers, with the technology stack covering Nvidia Confidential Computing, Intel TDX, and Google Titan chips. Apple emphasized that it still controls the PCC software, devices only trust programs approved via Apple's encryption, and the relevant binary files will be open for inspection by security researchers.

Apple didn't truly give up control, but it gave up the dignity of complete in-house development.

Bones Are Borrowed

To understand Apple's position in the AI era, one must first identify its most core asset.

It's not the chip, not the model; it's the device. The device holds the photo album, email, calendar, maps, and payments, carrying the fragments of many ordinary people's lives. Whichever AI can mobilize these fragments becomes more than just a chatbot; it becomes a true personal intelligence hub.

Apple started laying the groundwork for this hub long ago. Workflow, acquired in 2017, later became Shortcuts, deeply integrated with Siri and system automation. App Intents, launched in 2022, allowed third-party apps to expose their capabilities to the system's entry points. In the Apple Intelligence era, these interfaces have become the hands and feet for AI to interact with the real world.

With these interfaces, OpenAI can come in, Gemini has come in, and the Chinese market can find local partners in the future. But they don't come in by directly taking over the iPhone; they are embedded within Apple's permission framework and privacy rules.

What Apple fears most isn't someone having a better model than its own. It fears users bypassing the system and handing their lives over to another entry point. If one day users stop opening apps and instead use an AI assistant that can manage everything for them, Apple would be relegated to a nicely made shell.

So from now on, the "Apple" in Apple Intelligence represents more product control rights, but no longer complete technological sovereignty. The skin is its own, the clothes are tailored in-house, but the bones are borrowed. Google provides the skeleton, Nvidia provides the joints, and Apple's job is to dress this body in its own clothes and send it out.

Google gains a massive endorsement from this deal—even Apple acknowledges Gemini's superior underlying capabilities. Nvidia gets proof that even with the best consumer-grade chips and aspirations for its own servers, for cutting-edge inference and complex agent tasks, GPU clouds remain unavoidable.

But the more bones you borrow, the less the body is entirely your own. Behind every borrowed bone lies the supplier's commercial calculus, regulatory concerns, and technological cadence. If one day someone needs to take the bones back, can Apple stand on its own? It doesn't need to answer that question just yet, but it will have to eventually.

A New Tenant Living in the System

Ordinary people don't care about model parameters. What they care about is whether their phone can bother them a little less.

At WWDC 2026, Apple said on stage: "There are times when you expect more from Siri."

For Apple, this was almost an apology.

Then it tried to show you a different kind of morning.

You wake up to a screen stacked with twenty notifications. In the past, you had to swipe through them one by one. Now, the system has already prioritized them for you, putting the boss's message at the top and collapsing ads and promotions into a single gray line. You open an email; a long work email has been summarized into three sentences. You decide to reply, and Siri drafts a response based on your usual tone with that person. You remember you need to call a store in the afternoon to return a product, but before you even dial, the system has already retrieved the order number from an email you received two days ago and pastes it onto the call interface.

This is the story Apple wants to tell: a layer of intelligence beneath the system, handling the cognitive clutter you face daily. Reading fewer useless things, spending less time searching for files, and getting interrupted by notifications less often.

To tell this story effectively, Apple nearly rebuilt the Siri entry point. On the iPhone, it's nestled in the Dynamic Island; swipe down to talk. On the iPad and Mac, it's combined with Spotlight. It has its own app, capable of saving and continuing past conversations, syncing across devices via iCloud. Apple wants Siri to be an AI assistant that lives in the system, possessing memory and context, but tries not to make it look like ChatGPT.

Vision is also an important direction. The camera gets a new Siri mode. Point it at food, and it gives you nutritional information. Point it at something you don't understand, and it identifies and searches for it. System-wide dictation is no longer just speech-to-text; it automatically adds punctuation and formatting, turning spoken language into sendable text.

The developer side is also being paved. Apple has opened up the Core AI framework, allowing third parties to load their own models on the device. The upgraded App Intents makes it easier for Siri to understand third-party apps. The Foundation Models Framework no longer just calls Apple's own on-device models but also supports integration with external providers like Claude and Gemini. Apple is paving a path for the entire ecosystem. In the future, for Siri to perform cross-app tasks, developers must expose their content and actions for the system to understand.

If these plans materialize, Apple AI will be more than just "a chatty Siri."

But this time, Apple is much more cautious than in the past. Siri AI will only be available to users in beta later this year, starting with English. And the same Apple Intelligence in China might not be the same product at all.

For Chinese users, Apple's AI is mostly just for show. The conference is exciting, the features look good, but it's "not yet supported in China."

The Chinese market has a full set of rules for generative AI, including content filing, safety requirements, and data localization. Apple needs to find a local model partner and pass regulatory approvals. Apple Intelligence in China might not just be delayed by a few months; its underlying architecture might be completely different.

US users see a combination of in-house models and Gemini. Chinese users might see a version created by kneading together Apple's system permissions, local cloud services, a local model, and regulatory requirements. They are all called Apple Intelligence, but their actual capabilities and reachable boundaries could be vastly different.

iCloud services in mainland China are operated by GCBD (Guizhou Cloud Big Data). The cloud stores files, AI needs to understand them; the cloud stores photos, AI needs to interpret them; the cloud syncs notes, AI needs to extract your plans, habits, and relationships from them. This data has completely new uses in the AI era and naturally faces different levels of regulation.

The more realistic threat comes from competition. Domestic phone manufacturers are moving very fast with on-device large models, Chinese language assistants, and imaging AI. For Chinese users, spending a significant amount on a new iPhone, only to find its core AI features unavailable, might lead them to switch brands.

The daily scenarios in the Chinese market are particularly tricky for Apple. WeChat, Alipay, Meituan, Douyin, ride-hailing apps, government services, hospital registration – these are the things many people truly use their phones for every day. If an AI assistant can't access these scenarios, can't understand group chats, receipts, verification codes, and the myriad expressions only locals instantly get, it can hardly be called "intelligent."

Understanding a Person

Apple Intelligence also has another problem: it won't be available on all iPhones.

iOS 27 can cover the iPhone 11 and the second-generation iPhone SE, but Apple Intelligence requires at least an iPhone 15 Pro or later, or an M-series iPad and Mac. The most powerful on-device models have even higher requirements: iPhone 17 Pro, iPhone Air,