Summary of Key Points
Apple's 2026 WWDC event marked Tim Cook's last appearance as CEO. The launch of "Siri AI" disappointed the market significantly due to its limited capabilities (only basic question-answering functions, without any "Agent" features), resulting in a loss of over $230 billion in the company's stock value shortly after the announcement. Looking back at the past two years, Apple has repeatedly delayed its commitments in the AI field, failing to develop strong in-house models and ultimately partnering with Google to integrate Gemini. Its closed ecosystem, which once served as a competitive advantage in the mobile era, has become a bottleneck in the AI age—privacy policies restrict data collection, and the pace of innovation is slow. Now, Apple is attempting to "open up" and seek self-rescue by allowing third-party AI models to be integrated into its systems and forming an alliance with Google. However, it still faces risks such as user loss and dependence on external models. The new CEO, Tim Cook (with a hardware background), aims to leverage the combination of "chips, devices, and ecosystem" to give Apple a chance to gain a competitive advantage in the AI landscape.
1. Why Did This "AI Test" Cause Such a Fuss in the Market?
At WWDC 2024, Apple promised a "completely new AI experience," but it took two years to release Siri AI—features that were already available from other platforms: conversation history (similar to ChatGPT), image recognition (Google Lens), and image generation (Midjourney). Even the much-anticipated "Agent" capabilities (such as automatically booking meals or managing schedules) are still in the planning stage. Internal data shows that the success rate of Siri's new functions is only 60-80%, with one out of five interactions failing. Additionally, users in the EU and China were excluded from the upgrade. The market's expectations for a major AI breakthrough were met with disappointment, leading to a $230 billion drop in Apple's stock value on the same day, equivalent to the market value of a Tesla.
2. Why Is Apple Always Late with Its AI Initiatives?
The main reasons are the dual challenges of a closed approach and strict privacy policies:
- Lack of Data: Apple strictly limits the use of user data for model training, relying on third-party or synthetic data, which hinders model development (similar to an athlete relying on plain water and steamed buns for nutrition).
- Slow Decision-Making: Apple must balance compatibility across 2.35 billion devices and navigate varying global privacy regulations, making progress in AI a year-long process (for example, features promised in 2024 were not fully implemented until 2026).
- The Cost of Arrogance: In the past, Apple was known for setting standards (e.g., with the iPhone), but in the AI era, it has become a follower, struggling to catch up while others are already developing advanced features like Agents.
3. The Counterproductive Effects of Apple's Closed Philosophy in the AI Age
Apple's closed ecosystem, which controlled all aspects of hardware, software, and services, was beneficial in the mobile era (e.g., the smoothness of the iOS system and the security of the App Store). However, in the AI age, this approach has become a limitation:
- Inadequate Model Performance: Without sufficient user data, its in-house models struggle to perform even basic tasks (e.g., inaccurate alarm settings).
- Loss of Standard-Bsetting Power: While Apple once defined what a phone should be like, now standards in AI are set by companies like OpenAI and Google.
- Market Reactions: Investors prefer companies with clear AI capabilities, as Apple's stock performance lagged behind those with substantial AI revenue in 2025.
4. Is Apple's "Openness for Self-Rescue" a Compromise or a Wise Move?
To address its AI shortcomings, Apple has made concessions:
- Alliance with Google: Integrating Gemini into Siri (borrowing from a stronger competitor under the guise of "private cloud computing").
- Allowing Third-Party AI Models: For the first time, users can switch to third-party models like Anthropic Claude or DeepSeek to power Siri and image generation.
- Opening up the Developer Ecosystem: New APIs allow third-party models to be integrated into Apple's systems, similar to the success of the App Store.
However, there are risks: if users find Google's Gemini more useful than Apple's own models, they may switch directly to Google Assistant. Developers might also bypass Siri and use third-party solutions, potentially turning Apple into a mere intermediary in the AI market, losing control over its core technologies.
5. Can New CEO Tim Cook Turn Around Apple's AI Strategy?
With Tim Cook taking over from Cook (who led the development of the M chip and Vision Pro), Apple signals a shift towards a more integrated approach using "chips, devices, and ecosystem." His expertise in hardware could be an advantage, as Apple's M chips offer strong performance, and Vision Pro demonstrates potential in spatial AI. The challenges include:
- Reducing reliance on Google Gemini and developing competitive in-house models.
- Preventing user and developer churn due to the open ecosystem.
- Leveraging hardware advantages (e.g., Vision Pro) to redefine the standards for AI devices.
Tim Cook concluded his tenure with an "AI test," marking a shift from a closed development approach to more pragmatic collaboration. Whether Apple can successfully revitalize its AI strategy will depend on how well it combines its hardware strengths with its AI ecosystem. If successful, it could regain a competitive edge; otherwise, it may fall behind in the rapidly evolving AI landscape.