Apple’s Ambitious AI Play: Overpromising, Under-Delivering?

Apple has built its brand on innovation and seamless user experiences, but when it comes to artificial intelligence, the story is getting complicated. In the age of ChatGPT and smart assistants that can hold conversations or write code, many are asking: Is Apple overpromising and under-delivering on AI? This long-form analysis dives into Apple’s history with AI (especially Siri’s rocky road), recent AI announcements versus reality, how Apple stacks up against rivals like Google, Microsoft, and OpenAI, the impact of Apple’s closed ecosystem, and whether Apple is falling behind in the AI race—and what it needs to do to catch up.

Apple’s Early AI Foray: From Siri’s Rise to Stagnation

When Apple introduced Siri in 2011 on the iPhone 4S, it wow’ed the world. Siri was lauded as a breakthrough in voice-driven AI—a personable assistant that could answer questions and perform tasks with just a spoken command. At the time, Siri positioned Apple as a leader in consumer AI. Fast forward a dozen years and Siri’s reputation has flipped from leader to laggard. While Siri has seen some improvements over the years, it has failed to live up to its early promise, especially as competitors leaped ahead.

Siri’s struggles with natural language and context are well-documented. Even today, Siri often only reliably handles very simple tasks (setting a timer, sending a basic text) and frequently fumbles with anything more nuanced. Ask Siri a complex question or try a multi-step request, and the results can be hit-or-miss. For example, users still find that dictating a text and getting Siri to send it to the right person can be an adventure in frustration. Siri’s ability to maintain context in a conversation is almost non-existent – it tends to treat each query in isolation. In an era when AI chatbots can engage in back-and-forth dialogue and recall context from previous messages, Siri’s limitations are glaring.

Part of the problem may be under the hood. Former engineers and reports have described Siri’s codebase as a “brittle,” “inflexible,” patched-together system that has been difficult to upgrade over time (Venture Beat) . Siri’s original architecture was never fully replaced, meaning new features were layered onto old foundations. This “spaghetti code” has reportedly made it arduous for Apple’s team to add major improvements like advanced language understanding or seamless third-party integrations. In essence, Siri’s brainpower was stuck in last decade’s design, even as AI technology evolved rapidly around it.

Apple’s own culture may have compounded Siri’s stagnation. The company is famously secretive and prioritizes a closed, privacy-centric ecosystem. In the years following Siri’s launch, Apple was noticeably absent from the broader AI research community – in 2015, Apple had published zero research papers on AI. By contrast, rivals like Google embraced open research and have published hundreds of AI papers yearly, sharing discoveries that fueled new innovations. Apple has started to open up a bit (it launched a Machine Learning Journal and by 2023 had a few hundred papers published), but its insular approach in the early 2010s meant missing out on some advances and talent circulating in the AI community.

In summary, Siri’s journey from a head start in 2011 to a bit of an afterthought today encapsulates Apple’s AI struggles. The company that once put a virtual assistant in everyone’s pocket is now often playing catch-up, hindered by legacy design decisions and a reluctance to tap into the wider AI world.

Recent AI Announcements: Hype vs. Reality

Despite past struggles, Apple clearly recognizes it needs to step up its AI game. In 2023 and 2024, Apple started dropping hints—and then big promises—about a new AI initiative. At WWDC 2024, Apple unveiled “Apple Intelligence”, an umbrella term for the company’s generative AI features across iPhone, iPad, and Mac. Apple executives touted it as a “personal intelligence system” that “harnesses the power of Apple silicon” and personal data to understand and create language and images, all with privacy at the forefront. Tim Cook himself hyped that “Apple Intelligence is generative AI in a way that only Apple can deliver”, emphasizing on-device processing and user privacy (Apple).

So what exactly did Apple promise? The announced features under Apple Intelligence were ambitious. Apple showcased new writing tools that could help users refine their writing or even rewrite whole emails for tone and clarity. Another much-touted feature was summarization: your iPhone would be able to summarize long messages or a “cluttered inbox” into a quick digest, saving you from information overload. Perhaps the crown jewel was a more powerful, context-aware Siri. Apple said Siri would gain “Personal Context”, meaning it could tap into your own data (like contacts, calendar, emails, and messages) to answer questions and perform actions tailored to you. Siri was also set to get “App Intents” – the ability to seamlessly jump into apps and perform multi-step tasks on your behalf. For instance, you might ask Siri to “Find the podcast John recommended to me last week and play it” and Siri would actually parse your messages, find the podcast link your friend sent, open your podcast app, and start playing it. These kinds of multi-app, context-rich tasks were aimed at making Siri far more useful than the relatively static assistant it has been.

Apple delivered these promises with plenty of fanfare. The first wave of Apple Intelligence features started rolling out in late 2024 with iOS 18.1. Users noticed things like the new writing suggestions in Mail and Messages, smarter autocorrect, and the ability to remove image backgrounds with a feature called Clean Up. Apple claimed this was just the beginning, with “many more [AI features] rolling out in the coming months”.

However, between announcement and delivery, reality hit. By early 2025, murmurs grew that Apple’s grand AI update wasn’t going as planned. Then Apple made a surprising admission: some of the most anticipated Siri improvements were not ready—and wouldn’t be for quite some time. In March 2025, Apple confirmed that key Siri AI features were delayed until 2026. In a statement, Apple said it needed “longer than we thought” to deliver a “more personalized Siri” that can take actions across apps. Keep in mind, Apple had never even given a firm public date for these upgrades at WWDC; internally, though, the goal was apparently to ship them in iOS 18.4 by spring 2025. Missing that internal deadline meant an embarrassing public delay.

The delay was not just a minor schedule slip. It struck at the heart of Apple’s credibility in AI. Apple had been advertising these AI-enhanced Siri capabilities in its marketing — even running TV commercials — for months, despite the features never reaching users. To tech observers, this was a serious misstep. It’s one thing to hype a concept but to actively promote non-functional features as if they existed is a dangerous game, especially for a company that prides itself on “it just works.” Tech journalist John Gruber didn’t mince words in a scathing piece titled “Something Is Rotten in the State of Cupertino.” He argued the real fiasco wasn’t just that Apple’s AI was late, but that “Apple pitched a story that wasn’t true” – essentially, Apple overpromised knowing they might not deliver. Gruber noted that Apple’s stellar reputation for delivering what it announces took a hit, saying the company’s credibility was “squandered” by hyping Siri features that turned out to be vaporware. In perhaps the most damning line, he concluded that the much-vaunted “more personalized Siri” features were, bluntly, “bullshit.”

Apple’s own fans and former loyalists echoed the frustration. Some labeled Apple’s AI vision a “fever dream” – a critique that Apple had talked up an illusion of grandeur without the execution to back it. Even insiders reportedly described the situation as “ugly and embarrassing” during Apple’s internal meetings. It’s a rare moment: Apple, typically a master of controlling narratives, found itself publicly admitting a failure in AI delivery.

Apple vs. the Competition: Losing Ground to Google, Microsoft, OpenAI

While Apple has been stumbling with Siri, the rest of Big Tech has been sprinting ahead in AI. In fact, the contrast is stark. Companies like Google and Microsoft (via OpenAI) have been showing off “mind-blowing” AI advances, leaving Apple’s efforts looking “relatively lackluster.” Let’s break down how Apple stacks up against its key competitors in AI:

Google: AI has been in Google’s DNA for years. Google Assistant (launched 2016) quickly outpaced Siri in conversational ability and the breadth of things it can do. Google has relentlessly improved Assistant with features like real-time language translation and continued context (remember the demo where Google Assistant made a phone call to book a haircut?). More recently, Google jumped on the generative AI wave with Google Bard, its answer to ChatGPT. Google is also weaving AI into virtually all its products—from Gmail’s smart compose to Google Photos’ magic eraser. Crucially, Google isn’t standing still on voice assistants either: it reportedly integrated its next-gen Gemini AI model into Google Assistant, aiming to make Assistant even more powerful and conversational. Google’s edge comes from its vast data and open research culture. It trains AI on the colossal index of the web and user data (with less hesitation than Apple), and it publishes research openly, attracting top AI talent. By comparison, Siri still can’t hold a fluid conversation or understand context the way Google Assistant can. In one illustrative test, a user asked Microsoft’s Bing (with AI from OpenAI) to list things it can do that Siri can’t; Bing rattled off examples like summarizing complex political situations or performing robust web searches. When the same user asked Siri what it can do that Bing Chat can’t, Siri’s answer was essentially instructions on how to…launch the Bing app. That says it all – Siri missed the point of the question entirely.

Microsoft (and OpenAI): Microsoft made a bold move by investing heavily in OpenAI (the maker of ChatGPT) and quickly integrating OpenAI’s tech wherever it could. By early 2023, Microsoft’s Bing search engine had an AI chat mode powered by GPT-4, turning the once-forgotten Bing into a buzz-worthy product. Microsoft didn’t stop at search: they rolled out Copilot, an AI assistant for Windows 11 and the Office suite, which can write emails in Outlook, create PowerPoint slides from an outline, or summarize documents in Word. Essentially, Microsoft brought generative AI into everyday productivity tools. The result is that Microsoft’s customers have AI assistance at their fingertips for work and creativity. OpenAI’s rapid iteration (from GPT-3 to GPT-4 and beyond) has kept Microsoft at the cutting edge. Meanwhile, Apple has nothing analogous to ChatGPT or Copilot in its lineup. Siri is the closest thing, and it’s nowhere near as capable in generating content or performing complex tasks. Apple’s AI features in 2024 were mostly on-device and relatively narrow (e.g. better autocorrect, email suggestions) rather than general-purpose AI that can code, compose, or converse at a high level.

OpenAI (and others): OpenAI itself, though not a platform company like the others, set the benchmark with ChatGPT. The sheer public enthusiasm for ChatGPT (which hit 100 million users in record time) showed what was possible when an AI feels truly smart. It can answer all manner of questions, write stories, debug code, you name it. This raised the bar for consumer expectations. Suddenly, Siri’s scripted jokes and canned responses felt antiquated. Other players like Amazon and Meta are also pushing hard. Amazon’s Alexa, which once was comparable to Siri, is now being supercharged with generative AI as well – Amazon announced a major Alexa overhaul with an LLM so it can handle much more free-form requests and even have more personality. Meta (Facebook) introduced AI chatbots (with personas) on Instagram, Messenger, and WhatsApp, and is using AI to generate fun stickers and filters  (MacRumors). In the broader AI ecosystem, open-source models are also emerging (like Llama 2 from Meta), which developers can use to build smart apps. Apple is conspicuously absent here.

In quantitative terms, Apple’s AI lags in several areas:

Natural Language Understanding: Google and OpenAI models understand nuanced queries and context far better than Siri. Siri often misunderstands or gives up in cases where ChatGPT or Google Assistant would succeed.

Generative Abilities: Need to draft an email or get a summary of a long article? ChatGPT/Bard/Copilot shine here. Apple’s latest OS can summarize text or suggest replies, but these features are limited and were only introduced very recently, whereas others have offered them for a while (and arguably with more sophistication).

Third-Party Integration: Siri largely works only with Apple’s own apps or a limited set of approved domains (like via Siri Shortcuts). In contrast, Google Assistant can interface with many third-party services (ordering an Uber, controlling your smart home devices, etc.), and OpenAI’s ChatGPT now has plugins that connect it to outside services. Apple’s closed approach meant Siri wasn’t even integrated with popular apps like Spotify for years. That’s changing slowly (Apple has added more “SiriKit” integrations), but it remains far behind the open ecosystems competitors enjoy.

Continual Improvement: AI models improve by training on massive amounts of data – user queries, web content, etc. Google and OpenAI essentially feast on the open internet (with some controversy, granted). Apple’s stance of not collecting too much user data and keeping things on-device means Siri hasn’t benefited from the same scale of learning. Google also benefits from feedback loops – e.g., every time billions of people use Google Search, it’s data to refine their algorithms. Siri doesn’t get that volume or diversity of cloud-based data due to Apple’s privacy stance.

Research & Innovation Pace: As noted, Google publishes hundreds of AI research papers a year and leverages a global research community. Apple’s more secretive R&D can’t tap into the collective brainpower as effectively. It’s telling that some of the biggest leaps in AI (transformer models, diffusion models for images, etc.) came out of Google, academia, or OpenAI – not out of Apple’s labs.

The net effect is that Apple is perceived as behind the curve in AI. Even Wall Street and tech pundits have started pointing this out openly. Microsoft’s and Google’s stock got fresh wind from their AI moves in 2023, while Apple faced questions about where its AI strategy was. Apple’s AI announcements (like Apple Intelligence) felt reactive – an attempt not to be left out of the “AI hype” conversation – whereas competitors were dictating the agenda. One columnist quipped that Apple’s AI strategy often feels like an afterthought compared to how central AI is to Google or Microsoft’s vision. That’s a dangerous place for Apple to be, given how critical AI is becoming in defining next-generation user experiences.

The Closed Ecosystem Dilemma: Privacy at the Cost of Progress?

A recurring theme in Apple’s AI story is the tension between its closed, privacy-first ecosystem and the data-hungry nature of modern AI. Apple has always worn its privacy stance as a badge of honor – “What happens on your iPhone, stays on your iPhone,” as one of their slogans went. This philosophy influences how Apple designs AI features, and it’s a double-edged sword.

On one side, Apple’s approach provides strong privacy protections. Siri and other features try to do as much computation on-device as possible, meaning your queries and data aren’t constantly being sent to the cloud for analysis. When Apple does need cloud processing for AI, it now uses techniques like Private Cloud Compute – essentially performing the computation on Apple’s servers but in a way that Apple claims doesn’t retain or leak your personal data. For example, if you ask Siri to summarize your emails, your device might encrypt that data, send it to Apple’s cloud for the heavy AI crunching, and get a summary back, all without Apple indexing or storing your emails. This is a very different approach from, say, Gmail’s server-side AI that directly scans your emails to offer suggestions (something privacy-conscious users might balk at).

The downside of this privacy-centric, walled-garden approach is that it inherently limits the AI’s capabilities. Modern AI, especially the kind powering things like ChatGPT, thrives on Big Data. These models are trained on hundreds of billions of words from websites, books, forums, you name it. They also often rely on cloud connectivity to fetch information or integrate services. Apple’s insistence on keeping user data siloed and minimizing cloud dependence means its AI has less fuel to learn from. As one analysis noted, Apple’s closed ecosystem limits the data Siri can access, making it hard for Siri to improve over time. Machine learning systems typically get smarter with more data and usage; if Siri isn’t “seeing” much data beyond what’s on each individual’s device, it’s learning much more slowly. Google and OpenAI, while raising valid privacy debates, undeniably have the advantage of scale – they leverage “anonymized” user interactions by the billions. Apple, by design, doesn’t leverage data the same way, and thus its AI models can lag in sophistication.

Another aspect is integration. Apple tightly controls Siri’s domain. Want Siri to do something in a third-party app? It only works if Apple has explicitly enabled an API for that and the app developer supports it. As a result, Siri stayed mostly confined to Apple’s own apps and a narrow set of third-party intents. In contrast, an AI like Google Assistant can freely interface with many Google services and, through Android’s intents or smart home standards, talk to a wide array of devices and apps. Likewise, ChatGPT’s open plugin ecosystem allows it to connect with services like OpenTable or Expedia to do things Siri simply can’t do under Apple’s closed model. Apple’s control can ensure quality and security, but it also means limited extensibility. If Apple’s team doesn’t build a particular skill into Siri, you’re out of luck; you can’t just install a “Siri plugin” from a third party to teach it new tricks (at least not yet).

Apple’s caution and secrecy in AI R&D also meant it missed out on the collaborative leaps seen elsewhere. Open-source AI frameworks and models have driven a lot of innovation (consider TensorFlow from Google, PyTorch from Facebook, or open models like Stable Diffusion). Apple historically has been slow to embrace open-source in AI. It’s telling that Apple only started participating in communities like Hugging Face (a hub for sharing AI models) in a minimal way—Apple has shared just a handful of models publicly, compared to hundreds by Google and Microsoft. This hesitance keeps Apple from benefiting fully from community-driven improvements. It also made Apple less attractive to some AI researchers who prefer an open environment to publish and collaborate.

To be fair, Apple’s stance comes from a principled place: user trust. They see themselves as the antidote to the data-harvesting operations of some peers. Apple often reminds us that privacy is a human right, and they design their products accordingly. Indeed, there is a trade-off: an AI that knows everything about you could be more helpful, but it could also intrude on your privacy in uncomfortable ways. Apple clearly decided to err on the side of privacy. The question is, can they deliver competitive AI experiences while holding that line? Or does keeping the garden walls high simply put Apple’s AI at an insurmountable disadvantage?

As of now, many critics believe Apple’s insularity has cost it AI leadership. The company that revolutionized personal tech with the iPhone now risks being seen as a laggard in the critical field of AI because it won’t (or can’t) leverage the approaches that competitors do. It’s a classic innovator’s dilemma: Apple’s differentiation (privacy, tight integration) is exacting an innovation tax in AI. The longer that remains true, the more Apple’s users might start looking elsewhere for smarter assistants and AI-powered tools.

Is Apple Falling Behind in AI?

Brutally put: Yes, Apple is currently behind in the AI race. The evidence is hard to ignore. Siri, once cutting-edge, is now often the butt of jokes. iPhone users who also use ChatGPT or Google Assistant can plainly see the gap in intelligence and usefulness. Apple’s AI features tend to be narrow and subtle (a smarter autocorrect here, a slightly better photo search there) rather than the bold, game-changing AI products coming from others.

Consider the public perception: When people think of AI in 2025, they likely think of ChatGPT conversations, Bing’s surprisingly creative answers, or Google’s AI-powered search results, more than anything Apple has done. Apple’s brand is still incredibly strong, but mainly for hardware design and overall ecosystem — not for being the best at AI. In fact, in some surveys or tech discussions, users lament that they carry the world’s most advanced smartphone (an iPhone with a neural engine and powerful chips), yet often turn to a non-Apple service (like ChatGPT) for anything AI-heavy beyond simple voice commands.

Even within the voice assistant domain, which Apple popularized, others have caught up or passed it. By late 2023, Amazon, Google, and even startups were incorporating large language models (LLMs) into assistants, making them more conversational and useful. Apple’s corresponding effort with Siri is delayed to 2026, as we discussed, which means for the next year or more, Siri will remain notably dumber than its rivals on many fronts. This is a strategic problem: Apple is effectively ceding that AI mindshare to competitors in the interim. And once users get used to a certain AI assistant or platform, they might stick with it.

Another sign of falling behind is the scramble we’re now hearing about from Cupertino. Apple has reportedly put AI on the top of its priority list internally. There are rumors of Apple spending billions on building out cloud infrastructure and even considering unprecedented partnerships. For instance, Apple is in talks with Google to possibly license Google’s Gemini AI model for use on Apple devices  – a stunning possibility, given the two are rivals, but it shows Apple might be willing to “buy” what it hasn’t been able to build fast enough. Similarly, Apple has discussed leveraging OpenAI’s technology in iOS, potentially embedding ChatGPT-like features in certain apps (MacRumors). These discussions imply that Apple knows its in-house efforts are not sufficient to deliver the AI prowess iPhone users expect in the next year or two. Apple never used to need to rely on competitors for core tech – the fact that using Google’s or OpenAI’s AI is on the table suggests a degree of urgency (even mild desperation) to catch up.

Apple’s enormous resources and talent shouldn’t be underestimated. The company is now investing heavily in AI R&D. Its hiring of John Giannandrea (ex-Google AI head) a few years back was a signal that it wanted to beef up AI. There are reports Apple has developed an internal large language model called “Ajax” with 200 billion parameters, supposedly more than GPT-3.5 (though not as good as GPT-4). But crucially, Apple has not turned this into a consumer-facing product yet. It’s one thing to have a big model in a lab; it’s another to integrate it into millions of devices in a way that’s useful and aligns with Apple’s privacy stance. So far, Apple has been extremely cautious on that front—perhaps too cautious, as competitors roll things out much faster.

The delay in Siri’s evolution also raises a cultural question: has Apple’s legendary operational excellence faltered in the AI realm? The company that rarely stumbles on execution (we’re used to Apple shipping polished products annually) has now very publicly stumbled. This has led some to call it a leadership failure. Critics from within the Apple community are asking why SVP John Giannandrea and even CEO Tim Cook haven’t delivered clear AI wins despite massive investments. It’s a fair question. Apple has no shortage of PhDs and PhDs, and it pioneered the Neural Engine in mobile chips, yet the breakthrough AI applications are coming from others.

In falling behind, Apple also risks losing developer mindshare. Increasingly, app developers are looking to integrate AI features (like an AI writing assistant inside a document editor, or an AI voice that narrates text). They will naturally gravitate to platforms and tools that support this. If Apple’s ecosystem makes it hard to plug in advanced AI (whether due to App Store rules, lack of APIs, or just inferior capability), developers might favor launching on the web or Android or Windows where they can tap into things like GPT-4 more freely. Apple did announce some APIs for developers to use Apple’s own language models on-device, but these are very new and likely not as powerful as what’s available via cloud AI services. A worst-case scenario for Apple is if the iPhone becomes seen as a poor platform for AI innovation relative to others. That could, over time, erode the iPhone’s dominance if not addressed.

All that said, Apple is not doomed in AI. It’s late, yes, but not out. The company has a track record of entering markets late yet still triumphing by doing things differently (think MP3 players, smartphones, smartwatches—Apple wasn’t first, it was just better in crucial ways). The optimist would say Apple could still do this with AI: perhaps they’ll unveil an AI assistant or companion so integrated with the Apple experience that it feels more useful and trustworthy than a generic chatbot in a browser. But as of now, that’s mostly hope. The current reality is that Apple is behind, by about a year or two in the generative AI surge, and by several years in voice assistant evolution. And the gap won’t close unless Apple makes some big moves.

What Apple Needs to Do to Catch Up

The silver lining of Apple’s AI predicament is that the company clearly recognizes the challenge and appears to be mobilizing to address it. Here are several steps (some already in motion, some that should be) for Apple to regain footing in AI:

1. Rethink Siri from the Ground Up: It might be time for Apple to rebuild Siri’s core architecture. If the old code and design are truly holding Siri back, a clean-slate approach could allow Apple to integrate state-of-the-art language models and more flexible dialog management. This is easier said than done (Siri is deeply embedded in all Apple devices and many languages), but incremental Band-Aids won’t enable the leaps needed. Apple could create a next-gen voice assistant that runs partly on-device and partly in the cloud (with privacy protections) that can truly understand context, and complex requests, and converse naturally. Essentially, make Siri 2.0 that is closer to a ChatGPT with a voice – while still able to do device-specific actions. Apple has already taken a small step by enabling “back and forth” conversation mode with Siri in iOS 18, but the scope of Siri’s understanding needs a huge boost.

2. Leverage Partnerships (Strategically): It’s somewhat against Apple’s DNA to use another company’s technology at a core level (they prefer owning the stack). But given the urgency, partnering might be the smart move. If Apple can integrate Google’s Gemini or OpenAI’s models into Apple’s ecosystem on Apple’s terms, it could instantly give iPhone users capabilities on par with Bard or ChatGPT. Imagine Siri quietly using an OpenAI-powered engine for general knowledge queries – users get the benefit of a giant LLM without leaving the Apple experience. Apple must tread carefully (to protect privacy and brand differentiation), but a partnership could be a shortcut to competitiveness. Even a temporary licensing of AI tech could buy time for Apple to develop its own models. The fact that talks with Google and OpenAI are happening means Apple isn’t ruling this out  (MacRumors). For Apple, which usually prefers in-house solutions, this openness is a significant and pragmatic shift.

3. Supercharge AI R&D and Talent: Apple needs to continue ramping up its AI research efforts, and perhaps swallow some pride by engaging more with the open AI community. This could mean publishing more and participating in conferences, sponsoring academic research, and even open-sourcing more non-critical parts of its AI work to gain goodwill and feedback. Apple has started to publish some of its AI work (hundreds of papers since 2017), but it’s still not seen as a hotbed of AI innovation in the same way as, say, Google’s DeepMind or Meta’s FAIR. Changing this perception might help lure top AI scientists who currently gravitate to places where they can publish and impact the field. Additionally, Apple has deep pockets – it should continue to acquire AI startups (as it has done with companies like Xnor.ai for edge AI, or VocalIQ for speech in the past) to bring in fresh technology and talent. Basically, money alone won’t solve AI, but strategic investment in people and research can.

4. Exploit Apple’s Hardware Advantage: One ace up Apple’s sleeve is its hardware. Apple designs some of the most advanced chips in consumer devices, with neural engines optimized for AI tasks. They should double down on this by ensuring their devices (iPhones, iPads, Macs with M-series chips) can run fairly powerful AI models locally. If Apple can get, say, a 10-billion-parameter model running efficiently on-device, it could enable many AI features without the internet or with low latency. They already demonstrated a 3-billion-parameter model for Apple Intelligence that can run on the device. Pushing this further, Apple could make the iPhone the best device for personal AI – an assistant that knows you and is always available, without constantly pinging the cloud. This plays to Apple’s integration strengths (hardware + software) and could differentiate them. Imagine an “AI mode” that uses the Apple Neural Engine to do intense tasks like image generation or translation right on the device. Apple has the ability to optimize AI models at the silicon level in a way others can’t.

5. Improve AI Developer Tools and Ecosystem: Apple should empower the vast iOS/macOS developer community to build AI-driven apps that shine on Apple platforms. This means providing great APIs for machine learning and natural language in Apple’s frameworks (Core ML, Create ML, etc.), and perhaps offering easy access to Apple’s own large language models for app developers. If every third-party app on the App Store can easily plug into robust AI capabilities, the overall Apple experience becomes more AI-rich. Also, if Apple allowed or encouraged some form of AI plugin system for Siri (akin to how OpenAI has plugins), it could crowdsource new abilities for Siri without Apple having to do all the work. Security is a concern, but maybe vetted plugins or shortcuts could be a start. The key is to avoid having Apple be the sole source of innovation; let others extend the platform’s AI in ways Apple might not have considered.

6. Set Realistic Expectations and Deliver: Last but not least, Apple needs to stop overpromising and regain user trust by delivering reliably. If that means not announcing an AI feature until it’s truly ready, so be it. Apple earned a reputation over decades that “it just works” and what they demo, they ship. They need to uphold that in AI as well. The next time Apple shows off a futuristic Siri capability, it should ideally be available in beta that day or in a confirmed update within weeks, not a nebulous “coming next year” (that then slips further). In short, under-promise and over-deliver for a while, instead of the reverse. This will rebuild confidence among both consumers and developers that Apple’s AI is on track. If delays happen (they sometimes do), be transparent as early as possible. The worst scenario is a repeat of 2024 where Apple markets something extensively that isn’t in consumers’ hands. A more humble and transparent approach will serve them well as they navigate this challenging period.

Conclusion: Can Apple Catch Up in the AI Race?

Apple finds itself in an unusual position: an underdog in a tech domain that is arguably the most important trend of the decade. For a company used to leading, not following, this must be uncomfortable. The criticism Apple faces – that it has overpromised and under-delivered on AI – has some merit, as we’ve seen. Siri’s evolution stagnated while competitors surged ahead, and Apple’s recent grand plans have been hampered by delays and execution issues. Yet, Apple has faced existential challenges before and come out swinging. The smartphone market was well underway when Apple entered it in 2007, and we all know how that turned out. The key will be whether Apple can adapt its philosophy to the realities of modern AI, bridging the gap between its privacy-first ethos and the capabilities users now expect.

The next few years will likely determine Apple’s AI trajectory. Will we see an “iPhone moment” for Siri or AI on Apple devices – a breakthrough that redefines the field on Apple’s terms? Or will Apple quietly license a solution and downplay AI hype, focusing instead on what it does best in hardware and OS polish? For tech enthusiasts, it’s a fascinating drama. Apple undoubtedly has some of the brightest minds and a war chest to fund whatever needs funding. If Apple can combine its strengths (integration, hardware, UX design, privacy) with truly cutting-edge AI, it could offer something unique – perhaps an AI that is deeply personal, trusted, and omnipresent across your devices, yet respects your privacy in a way others don’t.

However, catching up is imperative. AI is becoming the interface for computing tasks; it’s not a side feature you can ignore. If Apple doesn’t accelerate its AI efforts, the risk isn’t just that Siri remains a joke, but that the overall Apple ecosystem starts to feel dated. Users might drift towards platforms that offer smarter assistants and more AI-enhanced experiences. That’s a scenario Apple surely wants to avoid.

In the end, Apple’s AI ambitions will be judged by results. Talk and demos alone won’t cut it (and indeed have backfired recently). The company needs to wow the world again – this time with AI that genuinely improves users’ lives in the seamless, almost magical way that we expect from Apple. It’s a high bar, and Apple is late to attempt the jump. But if there’s one thing Apple has shown time and again, it’s that being late doesn’t matter if you ultimately get it right. The pressure is on for Apple to get AI right, and the tech community will be watching closely. Will Apple’s AI story be one of a comeback and renewal or a cautionary tale of hubris? The answers will unfold as Apple either catches up to its promises — or gets left behind in the next great tech revolution.