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Apple and AI : siri the early years

Babbage presents a startling narrative arc for a tech giant often viewed as invincible: Apple didn't just miss the generative AI revolution; it actively squandered a decade-long head start by betting on a flawed internal strategy before being forced to rely on its fiercest rival, Google. This piece is not a standard industry recap but a forensic audit of corporate DNA, arguing that Apple's historical strength in scaling hardware has become a liability when software intelligence requires a different kind of agility. For the busy listener tracking the next decade of computing, the critical insight here is that the "AI revolution" may not be won by the fastest mover, but by the one that can best integrate intelligence into the physical device without compromising privacy or performance.

The Dream and the Hardware Reality

Babbage begins by anchoring the discussion in the recurring patterns of technological history, noting that "Apple is one firm with a track record of acting to preserve and strengthen its DNA." The author identifies the appointment of John Ternus as CEO as the latest proof of this internal continuity. However, the commentary quickly pivots to the specific failure of Apple's first major foray into voice AI. The piece highlights the irony of the 2011 launch of Siri on the iPhone 4S, where the hardware was woefully inadequate for the promised software. As Babbage writes, "The iPhone 4S... used a dual core 32-bit Arm Cortex A9 processor... This was nowhere near powerful enough to even attempt the voice recognition part of Siri, let alone 'understand the meaning' of what the user was saying."

Apple and AI : siri the early years

This framing is effective because it shifts the blame from a lack of vision to a mismatch of execution. The article reminds us that the original promise was about semantic understanding, not just syntax. Phil Schiller, Apple's then-SVP of Marketing, famously told the audience, "we want to talk to it any way we'd like," and Scott Forstall demonstrated a system that "understands the meaning and then goes and gives me this weather forecast." Yet, as Babbage points out, the reality was a decade of "acute irony" where the assistant consistently fell short. The author draws a sharp parallel to the early days of Dragon NaturallySpeaking and Nuance Communications, noting that while those tools were powerful, they required users to learn a rigid syntax, whereas Siri was marketed as a natural language breakthrough that never quite materialized. The argument suggests that Apple's DNA of waiting for hardware to mature before shipping software, while usually a strength, left them vulnerable when the software itself became the primary differentiator.

The Agentic Miss and the ChatGPT Shock

The commentary takes a darker turn when analyzing the gap between Siri's original design and the modern definition of AI. Babbage notes that Siri was conceived as an "Agentic AI" tool, designed to execute tasks rather than just generate text. The piece cites an interview with Siri's founders, who described the vision as "intelligent agents... designed to do things for us, rather than simply paint pictures or answer questions." This distinction is crucial. While competitors like MidJourney and ChatGPT exploded in popularity by focusing on content generation, Apple's original mandate was action-oriented. Babbage argues that Apple "squandered the leading position it had when it acquired and then integrated Siri into the iPhone" by failing to evolve that agentic capability.

The arrival of OpenAI's ChatGPT in November 2022 served as a wake-up call that the article describes as a "world-changing technology" that left Apple "caught flat-footed." The author cites internal reports from 2023 revealing that Apple was working on a framework called "Ajax" and a chatbot dubbed "Apple GPT," yet there was "a lot of anxiety about this and it's considered a pretty big miss internally." This admission of internal panic is a key piece of evidence. Babbage suggests that the delay wasn't just technical but strategic, as the company struggled to reconcile its privacy-first, on-device processing model with the massive compute requirements of large language models. Critics might note that the pressure to maintain on-device privacy is a legitimate competitive moat, not just an excuse for delay, but Babbage's point stands that the execution lagged behind the market's expectations.

The fiasco is that Apple pitched a story that wasn't true, one that some people within the company surely understood wasn't true, and they set a course based on that.

The Strategic Pivot to Rivalry

The most provocative section of the piece details Apple's eventual capitulation to the reality of the technology gap. After a "false dawn" at WWDC in June 2024, where the company promised "Apple Intelligence," the features were delayed in March 2025. The author notes that the announcement understated the severity of the situation, with the features being "nowhere near ready to ship." The turning point came in June 2026, when Apple officially announced a deep collaboration with Google. Babbage writes, "This year, we embarked on a deep collaboration with Google, leveraging the technologies behind their Gemini family of models."

This admission is framed as a historic irony: the company that spent decades building a walled garden against Google is now dependent on Google's models for its core AI experience. The article points out that Federighi stated the collaboration started in 2026, only after the failure of the internal "Apple Intelligence" initiative became clear. The author concludes that Apple has "squandered the leading position" and is now in a "disastrous" position to start the third decade of the 21st century. However, Babbage leaves a door open for a redemption arc, suggesting that Apple's ability to scale production and integrate silicon could still make them a "huge winner" if they can successfully adapt these external models to run efficiently on their hardware. The piece ends on a note of cautious uncertainty, asking why Apple could still emerge as a big winner despite this apparent failure.

Bottom Line

Babbage's strongest contribution is the reframing of Apple's AI struggle not as a lack of innovation, but as a failure of timing and a mismatch between their hardware-centric DNA and the software-defined nature of modern AI. The piece's biggest vulnerability is its reliance on a futuristic timeline (2025-2026) that reads as speculative fiction rather than established fact, which may confuse readers looking for a historical analysis of current events. However, the core argument—that the winner of the AI revolution will be determined by who can best integrate intelligence into the user's physical device—remains a vital lens for understanding the next phase of the tech industry.

Deep Dives

Explore these related deep dives:

  • Siri

    This article details the acquisition of the original startup by Apple, revealing how the technology was initially built as a standalone app with a different business model before being integrated into iOS.

  • Dragon NaturallySpeaking

    Understanding this early speech recognition pioneer provides essential context for the 'let-down' Phil Schiller describes, illustrating the decades-long struggle with syntax-heavy interfaces that Siri was meant to finally overcome.

  • Nuance Communications

    This entry explains the specific voice recognition engine that powered Siri's early backend, clarifying the technical debt and licensing constraints that shaped Apple's initial AI strategy and its eventual pivot to custom silicon.

Sources

Apple and AI : siri the early years

One of the reasons for studying technology history is to look for recurring patterns: characteristics of either the technology itself or of firms that are built around it that are repeated time and time again.

These can be particularly useful as predictors of future behaviour at a firm when that firm has strong DNA that is passed down from one generation of management to the next.

It’s generally agreed that Apple is one firm with a track record of acting to preserve and strengthen its DNA. The appointment of 25-year Apple veteran John Ternus to succeed Tim Cook as Apple CEO is just the latest and most prominent example.

Which brings us to the subject of this post and the next: Apple and AI.

Spoiler alert! These posts suggest that there is a credible scenario where Apple is a huge winner - possibly even the biggest winner - in the ‘AI revolution’.

I want to say upfront that there is too much uncertainty about the final shape of the technology underpinning AI to come to any firm conclusions.

I’d also like to acknowledge the post Apple is the King of AI and Nobody Knows It by Limited Edition Jonathan which prompted me to think about Apple and AI. I want to emphasise though that I’ll be considering Apple’s position from a very different perspective and that, in the end, I reach conclusions that, although aligned in some ways, are distinctive in others.

The core of the argument is that Apple has a track record of scaling the production of leading edge technology to make it available to its millions of customers ahead of its competition. This has been accompanied by long term investment across the whole technology stack, from custom silicon, through compilers and application software. These capabilities, and a willingness to use them, are central features of Apple’s DNA.

There is a lot more to the say about this though! We’ll go into (much) more of the detail that supports these arguments in the next post. In the rest of this post we’ll take stock of where Apple is now and its AI journey so far.

Siri Arrives: The Dream and The Reality.

… for decades technologists have teased us with this dream that you're going to be able to talk to technology and it'll do things for us. Haven't we seen this before over and over, but it ...