When John Ternus becomes Apple’s chief executive on Tuesday, he inherits a company that looks, on paper, unbeatable. Apple is valued at nearly $5 trillion and remains one of the most profitable businesses on Earth. That is also the problem. Starting at the summit is harder than climbing it. As The Wall Street Journal’s Rolfe Winkler has put it, Ternus must take Apple beyond Tim Cook’s iterative supply-chain mastery if the company is going to reach new heights from already extraordinary ones.

Ternus takes over from Cook, who will stay on as executive chairman. Cook spent 15 years turning Steve Jobs’s product company into a supply-chain, services, and cash-flow machine. He extracted enormous value from the iPhone, expanded the App Store and iCloud, and built Apple into a colossus. What he did not leave behind is an undisputed next act.

The question hanging over Cupertino is whether Ternus can do more than keep the machine running. Can he revive the kind of product-led risk that defined Jobs, without throwing away the operational discipline that made Cook’s era so lucrative?

The hardware lifer

Ternus is not a celebrity CEO. He is a mechanical engineer who joined Apple in 2001, rose through Mac hardware, and eventually ran engineering for iPhone, iPad, Mac, Watch, AirPods, and Vision Pro. He is 50 — the same age Cook was when he succeeded Jobs. Colleagues describe him as even-tempered, detail-obsessed, and fluent in Apple’s internal politics.

That profile is both his advantage and his constraint. He knows how Apple products are actually built. He helped shepherd the shift to Apple silicon, one of the most consequential engineering moves of the last two decades. He also oversaw incremental refinements that made devices thinner, more durable, and easier to repair. Those are real achievements. They are not, by themselves, a new category.

If Jobs was the visionary and Cook the operator, Ternus sits in the middle: a hardware savant asked to become a public strategist. He will have to lean on other executives for finance, sales, legal, government relations, and procurement — areas where Cook excelled.

The AI gap

Apple’s most visible vulnerability is artificial intelligence. Since ChatGPT arrived in late 2022, rivals have poured hundreds of billions of dollars into models and data centers. Apple has emphasized on-device intelligence, privacy, and integration with existing gadgets. That is a coherent strategy. It has not yet produced a moment that reset the industry conversation.

Ternus’s bet is that hardware is still the point. Apple’s argument is that people will use AI through devices they already love—phones, earbuds, glasses, home screens — rather than through chat windows alone. Camera-equipped AirPods, smart glasses, a voice-aware home display, a robotic-arm screen, and a wearable pendant are all in various stages of development. Some are expected this fall. Others are further out. All of them depend on making AI feel native, private, and physical.

That is a harder product problem than buying more GPUs. It is also the only problem Apple is uniquely positioned to solve. Whether investors will wait for it is another matter. Apple’s stock has lagged other megacap tech names for much of the generative-AI boom, even as its profits stayed enormous.

A first act already scheduled

Ternus will not get a quiet first month. Apple’s fall event is set for September 9th at the Steve Jobs Theater, under the tagline “Surprise and shine.” The centerpiece is widely expected to be Apple’s first foldable iPhone, alongside new Pro models. The timing was not accidental. Ternus led the hardware work on the foldable, and the CEO transition was aligned with its debut.

A foldable is not a new idea. Samsung has sold them for years. Apple’s version will be judged on crease, durability, software, and whether unfolding a phone feels inevitable rather than novelty. Reports describe a book-style device with a compact outer screen and a roughly tablet-sized inner display, priced well above $2,000. Early supply is expected to be tight.

That product is a test of more than engineering. It is a test of whether Apple can still introduce a form factor that changes how people use a phone — the last time it did that at this scale was the iPhone X in 2017.

What “innovation” actually means now

There is a temptation to frame Ternus as “the next Jobs” or “Cook 2.0.” Neither is useful. Jobs could force the company through a single product vision because Apple was smaller and more desperate. Cook could optimize a global manufacturing system because the iPhone had already won. Ternus faces a third problem: how to invent the next platform while defending a $5 trillion franchise, a China-centric supply chain under political strain, and regulators who treat Apple as infrastructure rather than a gadget maker.

The Jobs engine was never just “think different.” It was an integrated loop of hardware, software, and a willingness to kill good-enough products. Cook’s engine was iteration, predictability, scale, and services attached to devices people already owned. Ternus has to run both at once.

That means new devices that justify themselves, not annual spec bumps. It means an AI strategy that does not look like a late partnership bolted onto Siri. It means keeping Apple’s culture of secrecy and craft without freezing the company in the last successful decade.

Cook built the Apple colossus. Ternus has to decide whether the next decade is about defending it — or risking another product that makes the last one look incomplete. The foldable iPhone will be the first public answer. It will not be the last.
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MacDailyNews Take: Good luck, John. No pressure!

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As Tim Cook hands the reins to hardware engineering chief John Ternus in September 2026, the company faces its biggest leadership transition in 15 years. While Cook’s tenure delivered massive revenue growth and operational excellence, many former employees and analysts say Apple has lost the product-first, risk-taking design culture that defined the Jobs years. Fortune explores whether Ternus can shift the company from steady iteration back to true innovation.

Sebastian Herrera for Fortune:
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Matthew Rogers remembers Apple’s scrappy days when, as an engineer fresh out of college, he could debate ideas directly with Steve Jobs… “I was 23, 24 years old, and I interacted with Steve Jobs. That’s crazy, right? But that’s the organization he ran,” Rogers told Fortune from his Silicon Valley office (he now runs food-recycling startup Mill).

Back then, Apple’s hierarchy was much flatter. What made Apple special, he said, is that Jobs surrounded himself with consumer-minded product leaders who were expected to argue. The mandate was to build “the best thing out there” and take real risks. Some of Apple’s most iconic products came out of a skunkworks team across the street from the main office.

That much-mythologized era continues to shape — and perhaps burden — Apple as it navigates its biggest leadership transition in 15 years. Somewhere along the way, Apple lost a part of its design and product magic, Rogers and several former design and hardware employees, investors, and analysts said. The September handoff from Tim Cook to incoming chief executive John Ternus, who currently leads hardware engineering, has Rogers and other longtime Apple watchers wondering: Can the company reclaim its product-first soul?

Rogers, who also cocreated smart home company Nest with former Apple executive Tony Fadell, said Ternus now faces a defining test. “I’ll put it this way,” he said. “He has a tall task, but a big opportunity.”

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MacDailyNews Take: As we posted on X just yesterday:

Under Tim Cook, Apple systematically strayed from Steve Jobs’ core vision. Product Design, the very soul of the company Jobs rebuilt, was demoted, sidelined, and subordinated to operations, supply-chain efficiency, and services revenue. The result? Fifteen long years of polished, but profoundly boring iteration: thinner bezels, faster chips, incremental camera bumps, and endless “Pro” variants that rarely moved the industry forward the way the original iPhone, iPad, or even the aluminum unibody MacBooks once did.

Jobs put Design at the absolute top of the food chain. Jony Ive’s group wasn’t a service department; it was the engine. Cook flipped the hierarchy. Hardware engineering, operations, and finance called the shots (poorly; witness Luca Maestri famously rejecting calls to invest in AI early on which is why he’s now sidelined). Design became reactive instead of directive. The Vision Pro launched as an impressive, but overpriced, underbaked curiosity. After billions spent, the Apple Car evaporated. True category-defining leaps simply did not happen.

Now comes John Ternus, a product person, a hardware guy who has spent his career shipping actual devices rather than optimizing margins. Early signals are encouraging: bringing trusted hardware lieutenants back into the fold and already embedding himself with the industrial design group. That is exactly the right instinct.

If Ternus moves quickly to restore Design to its rightful place at the apex of Apple’s decision-making, the company might finally escape the long, comfortable, lucrative mediocrity of the Cook era. Apple doesn’t need another fifteen years of safer, prettier, or just different iterations. It needs the return of products that make people stop, stare, and rethink what a computer in their pocket, on their wrist, or on their face can be.

The hardware engineer is about to become CEO. The question is whether he has the will — and the authority with Cook looming as Executive Chairman — to put Design back on top where Jobs left it. The next few years will tell us if Apple can innovate again… or if the iteration treadmill simply continues under a new CEO.

Fingers crossed!

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Siri Wolfram Alpha integration helped create one of the defining moments in the early history of consumer artificial intelligence. When Apple introduced Siri with iPhone 4S in October 2011, users were not merely given another form of voice control. They could ask a spoken question and receive a calculated answer rather than a page of search results.

Questions involving mathematics, measurements, dates, nutrition, astronomy and factual comparisons could be routed to Wolfram Alpha, a computational knowledge engine launched in 2009. Siri handled the conversation, while Wolfram Alpha supplied structured information and calculations behind many of the responses.

The experience could feel remarkably advanced for its time. Asking how many days remained before a holiday, comparing the populations of two cities or converting miles into kilometers no longer required opening Safari, entering a query and selecting a website.

That direct path from natural language to a useful answer established an expectation that continues shaping AI products today.

Siri Began as an Action-Oriented Assistant

Siri did not begin inside Apple.

Its technological roots extend to research conducted at SRI International, including work associated with the DARPA-funded CALO project. The objective was to develop systems capable of learning, organizing information and helping people complete everyday tasks.

SRI later spun the technology into Siri Inc., founded by a team including Dag Kittlaus, Adam Cheyer and Tom Gruber. The company released a standalone Siri app for iPhone in early 2010.

The original product was designed around actions rather than ordinary web search. A user could ask for a restaurant reservation, request a taxi, check entertainment listings or search for local services. Siri attempted to understand the intent and connect the request with an appropriate online provider.

This approach was ambitious because the assistant needed to coordinate several different systems. Speech had to be converted into text, the request had to be interpreted, context had to be considered and an external service had to return something useful.

Apple acquired Siri in April 2010. Steve Jobs had personally recognized the potential of moving beyond a collection of apps toward a conversational layer capable of reaching them on behalf of the user.

When the assistant returned as an integrated feature of iPhone 4S, Apple had narrowed some aspects of the original startup vision while making the technology accessible to a much larger audience.

Siri could send messages, create reminders, place calls, manage calendar events, check weather conditions and answer questions. Apple initially labeled the feature as a beta, an unusual decision for a prominent part of a new iPhone launch.

The beta label acknowledged that natural-language interaction remained unpredictable. Accents, ambiguous phrasing, network conditions and external data providers could all influence whether a request succeeded.

Siri Wolfram Alpha Made Answers Feel Immediate

Siri Wolfram Alpha became especially memorable because it showed the difference between retrieving information and computing an answer.

A conventional search engine could locate pages containing a formula. Wolfram Alpha could apply that formula to the values supplied by the user. It could interpret units, compare quantities, calculate probabilities and organize factual data into a structured response.

That distinction allowed Siri to appear more knowledgeable than a voice interface simply reading search results aloud.

A request such as “What is 15% of $86?” could return the calculation directly. A question about the distance between planets could produce a value without requiring the user to inspect several websites. Wolfram Alpha could also answer unusually specific requests, including information about aircraft flying overhead when the necessary location and aviation data were available.

The system was not a large language model. It did not generate open-ended prose in the way current AI assistants do. Wolfram Alpha relied on curated data, symbolic computation, algorithms and formal representations of knowledge.

That structure provided an advantage that remains relevant: many answers could be calculated and traced through defined information rather than improvised statistically.

Early Siri combined several specialized capabilities instead of depending on one system to do everything. One provider might supply restaurant information, another could handle maps and Wolfram Alpha could process computational questions.

The assistant acted as the conversational coordinator.

This architecture made Siri seem like one intelligence even though the response could come from a network of separate services. Modern AI platforms have returned to a similar idea through tools, agents and external data connections. A language model may manage the conversation while calling a calculator, calendar, search engine or business system when a task requires reliable external action.

Siri demonstrated that model on a consumer smartphone more than a decade earlier.

A Small Integration Created a Large Expectation

The Wolfram Alpha partnership influenced how people began judging digital assistants.

Before Siri, voice features on phones were commonly associated with rigid commands such as calling a contact or selecting music. Siri invited users to speak more naturally and attempt questions that would previously have been typed into a browser.

The results were inconsistent, but successful interactions changed perceptions immediately. The iPhone appeared capable of understanding intent, reaching into a knowledge system and returning a concise answer through one conversational interface.

That apparent simplicity concealed substantial infrastructure. Speech recognition, language interpretation, location, personal context and external databases all had to work together quickly enough to preserve the feeling of a conversation.

Wolfram Alpha also gave Apple access to expertise that would have taken years to reproduce internally. Stephen Wolfram had spent decades building Mathematica and computational systems before launching the public knowledge engine.

Following the Siri announcement, Wolfram described the integration as a characteristically direct Steve Jobs decision: users wanted access to knowledge and actions without unnecessary intermediate steps.

That idea became more influential than any individual answer Siri provided.

The assistant did not need to show how many services had participated. It needed to understand the request and return the result.

Early Siri Also Revealed the Limits of the Model

The first version of Siri could feel futuristic one moment and frustrating the next.

Some questions reached Wolfram Alpha correctly, while similar wording could trigger a web search or an unrelated response. Siri occasionally struggled to determine whether the user wanted a calculation, a local result or an action inside the phone.

Wolfram Alpha responses were also better suited to some subjects than others. Mathematical and scientific questions matched its computational structure, while casual conversation, current events and subjective requests required different sources.

Network dependence created another weakness. Early Siri processing relied heavily on remote servers, so delays or outages could make a basic request fail even when the iPhone itself was functioning normally.

Those limitations foreshadowed the central challenge surrounding modern assistants. A convincing interface encourages people to ask almost anything, but no single technical system performs equally well across every type of request.

Current generative AI can produce more natural language and handle a wider range of topics. It can also create confident errors when factual grounding is weak. The computational approach represented by Wolfram Alpha remains valuable precisely because some questions require verified data and exact calculation rather than fluent speculation.

The story behind the first Siri steps is therefore not only about an old iPhone feature. It shows that a powerful assistant needs more than a conversational personality. It requires access to dependable tools, structured knowledge and the ability to recognize when another system should provide the answer.

Siri Wolfram Alpha offered that lesson in 2011, long before AI assistants became capable of writing essays, interpreting screens and acting across apps.

The post Siri Wolfram Alpha Defined the First AI Assistant appeared first on AppleMagazine.