Inside Apple’s AI Struggles: How Executive Turmoil and Technical Roadblocks Threaten the Company’s Future

The Truth About Apple's AI Collapse: From Jobs' Vision to Executive Mistakes

Artificial intelligence has been a dominant topic for nearly three years, with tech giants rushing to stake their claim. Yet, Apple—arguably the company most embedded in our daily lives—appears to lag far behind. Despite introducing Apple Intelligence at WWDC last June, most users have yet to see meaningful AI features from Apple. The world watches as Apple’s AI efforts falter, but few understand why.

Recent reports reveal Apple's internal struggles, conflicting strategies, and technical challenges that have stalled its AI progress. This article breaks down Apple’s AI journey, from its early promise to present-day hurdles, to help product development professionals grasp where Apple stumbled and what lies ahead.

01 Siri: Early AI Promise, But Stagnant Progress

On October 4, 2011, Apple launched Siri alongside the iPhone 4s, just a day before Steve Jobs passed away. Siri was groundbreaking—a voice assistant capable of booking restaurants, finding theaters, or calling taxis. It marked a significant step in making futuristic tech mainstream.

Jobs didn’t believe in traditional search engines. He thought Apple’s role was to deliver carefully curated content instead of letting users search actively. This philosophy shaped Apple’s approach to AI for years.

When Jobs first encountered Siri, he saw beyond it being just an app. Siri’s goal was to let users "talk to the Internet" and have the assistant handle everything seamlessly—a vision that resonates strongly with today’s large language model (LLM) applications.

Apple quickly acquired the Siri team after persistent negotiations and made it a top priority. Yet, while Siri initially led the smart assistant market, competitors like Google, Amazon, and Xiaomi soon overtook it with more advanced voice assistants and smart speakers.

Apple focused its AI efforts elsewhere—on facial recognition, fingerprint sensors, intelligent suggestions, and hardware projects like head-mounted displays and autonomous cars. It made several AI-related acquisitions but notably passed on Mobileye, which later became a leader in autonomous driving tech.

Despite early AI investments, Siri remained largely unchanged for years, primarily handling basic tasks like setting timers or playing music. The company missed key advancements that competitors capitalized on.

02 Expectations Clashed with Internal Conflicts

Apple’s troubles in AI are rooted in internal disagreements and shifting leadership. In 2018, Apple hired John Giannandrea (JG) from Google, expecting him to lead a transformation in AI. JG was a top AI executive with a strong track record, and Apple’s CEO Tim Cook publicly expressed high hopes for AI’s role in Apple’s future.

However, seven years later, those hopes remain largely unfulfilled. Apple’s AI efforts stumbled due to inconsistent views among executives. Some pushed for AI to be a major feature of iOS, but Craig Federighi, who oversees software engineering, resisted heavy AI investment.

Cook has been an optimistic advocate for AI and is frustrated by Siri’s shortcomings and Apple's weak position in smart speakers. But internal friction slowed progress.

JG initially believed Apple’s closed ecosystem was a strength, allowing rapid deployment of AI features. Yet he soon realized Apple needed massive investments in data annotation and infrastructure for training large models—investments that didn’t align with Apple’s traditional product development approach.

JG’s attempts to streamline Siri and focus on core functions faced resistance. Federighi and other leaders doubted AI’s importance on mobile devices, preferring Apple’s usual strategy of late entry and refinement rather than early bets.

The launch of ChatGPT in late 2022 blindsided Apple. Before then, the concept of a dedicated AI assistant was barely on Apple’s radar. Only after the ChatGPT wave did Federighi embrace generative AI, pushing for AI features in iOS 18 and forming a team to develop large language models—years behind competitors.

Fragmented ownership of AI projects within Apple led to misalignment and slow progress. At WWDC 2024, Apple Intelligence arrived but fell short of expectations. Internally, Apple’s AI chatbot trails ChatGPT by at least 25% in accuracy and capability.

Negotiations over partnerships with competitors like Google and OpenAI caused further internal disputes. JG preferred Google’s Gemini for better privacy and sustainability, but others supported OpenAI, resulting in Apple routing Siri’s unhandled requests to ChatGPT.

JG’s cautious stance on generative AI—believing consumers don’t want chatbot tools like ChatGPT—limited Apple’s consumer-facing AI ambitions. His outsider status and perceived leniency in execution created friction within Apple’s tight-knit executive team.

Earlier this year, JG was stripped of product development control, though he retained oversight of AI research and large model development. Despite challenges, JG remains at Apple, committed to setting the AI effort on a better path.

03 Technical Constraints and Privacy at Odds

Apple’s AI shortcomings aren’t solely due to leadership. Hardware decisions also played a role. Despite its resources, Apple was slow to acquire AI-specific GPUs, losing ground to Amazon and Microsoft who secured most of the hardware needed for AI model training.

Apple employs fewer AI experts and invests less in infrastructure than competitors, hindering its ability to build competitive AI models.

Apple’s cautious approach is partly strategic—letting others validate technologies before rolling out polished products has historically worked well. CEO Cook recently stated that Apple is taking more time to meet its quality standards, but the question remains: how much time is too much, especially in a fast-moving field?

Another key challenge is Apple’s strict privacy stance. While its commitment to user privacy has been a selling point, it limits access to valuable data needed to train powerful AI models. Unlike competitors who leverage vast user data, Apple relies on licensed and synthetic datasets, slowing AI development.

This trade-off between privacy and data access has become a major bottleneck. Apple’s marketing strength in privacy now constrains its technical capabilities in AI.

04 The Ripple Effect: AI Challenges Impact Apple’s Future

Apple’s AI struggles affect more than just Siri or Apple Intelligence. The company recently ended its multibillion-dollar autonomous driving project, partly due to AI shortcomings in self-driving capabilities.

Future product lines—augmented reality glasses, robotics, and smart wearables—depend on advances in AI. Failure to integrate AI effectively threatens Apple’s core philosophy that hardware and software must work seamlessly together.

Eddy Cue, Apple’s Senior VP of Internet Software and Services, warned that Apple’s dominance is at risk. Declining searches on Apple devices hint at users turning to AI models for information instead of traditional search. Cue even suggested the iPhone could become irrelevant within a decade if Apple fails in AI.

External pressures add to the urgency. New EU regulations may require Apple to allow users to switch Siri to third-party voice assistants. Without a competitive AI assistant, Apple risks losing users to offerings from OpenAI, Google, Meta, and others.

Apple is responding. Its AI office in Zurich is developing “LLM Siri,” a version based entirely on large language models to make Siri more conversational and capable of integrating information. Thousands of analysts worldwide review AI outputs to reduce bias and hallucinations.

Some executives want Siri to compete directly with ChatGPT, opening access to the web and multiple data sources. Internal testing shows promising progress, with some believing it matches recent ChatGPT versions.

Still, at the upcoming WWDC, Apple plans only modest AI updates—battery management optimizations and virtual health coaches—with no major Siri overhaul. Apple is reportedly preparing to market Apple Intelligence separately from Siri to avoid tarnishing AI marketing with Siri’s lagging reputation.

Key Takeaways for Product Development

• Alignment is critical: Internal disagreements and fragmented ownership of AI initiatives slow progress. Clear leadership and unified direction are essential.
• Invest early and decisively: AI requires significant upfront investment in talent, infrastructure, and data. Apple’s conservative approach delayed its competitiveness.
• Balance privacy with data needs: Strict privacy policies can hinder AI training. Finding ways to protect users while enabling AI innovation is vital.
• Adapt to fast-changing markets: Unlike hardware, AI evolves rapidly. Late entry strategies may not work for AI products.
• Product integration matters: AI must enhance core products and user experience rather than exist as isolated features.

Apple’s AI story offers valuable lessons for product teams. Success depends not just on vision, but on execution, investment, and adapting company culture to new technology demands.

For those looking to strengthen their AI skills and better navigate such challenges, explore Complete AI Training's latest AI courses tailored for professionals in product development and beyond.