Apple has carved out a prestigious position in the consumer tech market through sleek designs, user-friendly interfaces, and a robust ecosystem. The data is clear - Apple is the market and perception leader in every category that matters. But developments in Large Language Models have presented the first significant threat to Apple's hegemony in decades.
I can't help but approach the trend of LLM absolutism with a dose of scepticism. The tech world tends to latch onto the 'next big thing' with almost evangelical fervour, overlooking nuance, complexities and pitfalls. There are impressive capabilities, but LLMs are not a panacea for all technological challenges. They come with their own set of limitations, including biases in their training data, a lack of true understanding, and potential privacy concerns. The overreliance on LLMs is already stifling tech solutions as companies prioritise AI-driven approaches at the expense of human creativity and problem-solving methods.
My concerns aside - it's clear that LLMs are not a flash in the pan. Driven largely by OpenAI's GPT series, LLMs are altering how we interact with technology. Text and speech-driven models "understand" and generate human-like content and instructions, enabling a new level of interaction between users and machines. They're already anticipating needs, personalising experiences, and creating a seamless digital interaction that feels almost human.
While companies like Google, Microsoft, Samsung, Meta, and smaller companies and startups have (perhaps too quickly) integrated LLMs into their products and services, Apple has lagged - at least publicly. This delay is not a matter of being a few steps behind in a race; it’s part of a paradigm shift in which Apple could find itself playing catch-up in a field it once led.
The integration of LLMs in competitor products is leading to more intuitive, personalized, and engaging user experiences than Apple currently offers. Tech Threads is full of scenarios where a user asks a Google-powered device a complex question and receives a nuanced, context-aware response, while Siri provides a standard, limited answer - or fails to process the request at all. This experience is becoming increasingly common, eroding one of Apple's key competitive advantages.
The force of LLMs extends beyond consumer-facing products. Through GitHub, Copilot, etc., LLMs streamline the developer ecosystem, where Apple has traditionally excelled. LLMs can assist in coding, debugging, and even designing software. If Apple's ecosystem lacks these tools, developers will gravitate towards more AI-integrated platforms.
So, the gap is real. That’s unarguable. But Apple's relative silence on the LLM era does not necessarily imply inactivity; behind the scenes, the company is making strides. In collaboration with Cornell University, researchers from Apple have quietly made a significant contribution to the field of Large Language Models (LLMs) with the open-source release of "Ferret" in October.
This multimodal LLM, unique in its ability to use regions of images for queries, marks a small but potentially transformative step in AI research. Despite a quiet debut on GitHub, Ferret has started to garner attention within the AI community - albeit without the typical fanfare associated with AI product drops.
Ferret, alongside Ferret-Bench, was released on October 30, with subsequent checkpoint releases unveiled on December 14. Apple Insider commented that the development initially went unnoticed in the broader tech community, but its significance was highlighted in a report by VentureBeat. Bart De Witte, who heads an AI-in-medicine non-profit, brought attention to this "missed" release through a post on X. He lauded it as a "testament to Apple's commitment to impactful AI research," suggesting a deeper, more strategic investment by Apple in both AI and LLMs.
This understated progress offers a hint at next year's Worldwide Developers Conference. Apple has historically used this platform to unveil major innovations and updates. It's plausible that Apple will reveal more substantial advancements in AI and LLMs, making the next WWDC a pivotal moment in tech. And Apple could once again assert its role as a leading innovator and disruptor.
I have reservations about Apple simply jumping onto the GPT bandwagon. I want Apple to maintain its unique approach to technology, which has always been about more than adopting the latest trend. Apple's strength lies in its ability to integrate new technologies to enhance and align with its existing ecosystem without compromising the core principles that define its brand – simplicity, user privacy, and an intuitive user experience.
Apple's emphasis on user privacy presents an opportunity for them to innovate in how LLMs are implemented. They are one of the few tech companies that could develop legitimate new AI data usage and privacy paradigms, setting much-needed industry standards. The goal should be to harness the power of LLMs in a way that respects user data and trust, which have always been cornerstones of Apple's relationship with its customers.
My hope is that if Apple does decide to integrate GPT or any other Large Language Model technology, it will do so with a thoughtful consideration of how it truly benefits the user. This means not just incorporating LLMs because they are the current technological vogue but assessing how they can enrich the Apple experience. Ideally, this would involve a seamless product fit where the technology feels like a natural extension rather than a forced addition.
