MacBook Neo helped Apple move against the global PC market in the second quarter of 2026, delivering double-digit Mac shipment growth while most leading computer manufacturers sold fewer units than a year earlier.

Preliminary data from IDC estimates that Apple shipped 6.7 million Macs during the quarter, up 10.1% from 6.1 million in Q2 2025. Its worldwide market share rose from 8.5% to 9.9%, placing the company within a fraction of the 10% threshold.

The rest of the industry moved in the opposite direction. Global shipments fell 4.9% year over year to 68.2 million units, ending nine consecutive quarters of growth. A separate Omdia estimate placed the decline at 3.6% and Apple growth at 15.9%, but both research groups identified the same pattern: the Mac gained ground while established Windows PC vendors contracted or remained flat.

IDC directly connected the Apple increase with the arrival of its lowest-priced notebook. MacBook Neo began shipping in March, giving the model its first complete quarter of retail availability between April and June.

MacBook Neo Drives Mac Shipment Growth

MacBook Neo entered the market as an alternative for buyers previously priced out of the Mac lineup.

The 13-inch notebook launched at $599 with an A18 Pro chip, 8GB of unified memory, a Liquid Retina display and up to 16 hours of video playback. It also brought macOS, Apple Intelligence and integration with the iPhone ecosystem into a price range commonly occupied by mainstream Windows laptops and Chromebooks.

That positioning attracted students, families and first-time Mac customers who did not need the performance or display technology of a MacBook Air or MacBook Pro. The four-color aluminum design also gave the entry model a more recognizable identity than the older Mac notebooks that frequently remained on sale through discounts.

IDC had already identified the computer as an unusual source of strength when it revised its notebook forecast earlier in 2026. Demand exceeded initial expectations in several markets, and reported inventory shortages appeared shortly after the March launch.

The effect became much more visible in Q2. MacBook Neo was available for the entire three-month period, allowing Apple to increase production, expand retail distribution and serve customers who could not obtain the computer during its first weeks.

The entry model also widened the Mac lineup without replacing the MacBook Air. Buyers seeking more memory, faster ports, longer wireless web use or additional external-display support could continue moving toward the M5-powered Air, while the less expensive machine handled education, office work, web browsing and communication.

That separation allowed Apple to reach lower price points without reducing the value of its premium notebooks.

Lenovo, HP and Dell Lose Shipments

Apple remained the fourth-largest traditional PC vendor, but its performance contrasted sharply with the companies above it.

Lenovo retained first place with 16.6 million units and 24.4% market share. Its shipments declined 2.1% from the previous year.

HP experienced the steepest drop among the top five, falling 9% to 13 million units. Dell shipped 9.3 million computers, representing a 5% decline. Asus remained nearly flat at 5 million units, up only 0.2%.

Manufacturers outside the five largest brands collectively suffered a 10.5% contraction, indicating that weaker demand and component inflation placed even more pressure on smaller companies.

The figures measure shipments into distribution channels or directly to end users, not confirmed retail purchases by individual consumers. A manufacturer can therefore record shipment growth while some inventory remains unsold in stores or warehouses.

MacBook Neo demand appears to have been strong enough to support Apple production increases rather than simply filling channels. The company reportedly doubled its annual production target after the initial response, although later quarters will reveal how much of Q2 reflected lasting demand rather than launch-period enthusiasm.

Apple also benefits from operating at a scale that provides leverage with memory, storage and semiconductor suppliers. Smaller PC brands face greater difficulty securing components at competitive prices when shortages intensify.

Memory Costs Reverse the PC Recovery

The Q2 contraction arrived after manufacturers and commercial customers accelerated purchases during late 2025 and early 2026.

Buyers expected memory and storage prices to rise, encouraging some businesses to replace computers earlier than planned. Vendors also increased inventories while components remained available.

That activity supported nine quarters of market growth but borrowed demand from later periods. Once the early purchases were completed, the industry faced a more difficult combination of expensive components, cautious consumers and elevated retail prices.

The memory shortage is closely connected to the AI infrastructure boom. Chipmakers are dedicating more production capacity to high-bandwidth memory used by data center accelerators, limiting the supply of conventional DRAM needed for personal computers.

IDC expects little relief before the end of 2027 or early 2028. The research company forecasts an 11.3% decline in worldwide PC shipments across 2026, with the fourth quarter potentially falling 20% from the previous year.

Computer revenue may not decline at the same pace. Manufacturers have raised prices faster than shipments have fallen, allowing them to collect more money from fewer units.

That strategy creates another problem. Retailers and distributors can become trapped with expensive inventory if customers refuse the new prices, forcing promotions or production reductions later.

MacBook Neo initially softened that pressure by giving Apple a new product near the bottom of the notebook market. Its original $599 price made the Mac competitive with midrange Windows machines at the moment many rivals were becoming more expensive.

The Price Increase Creates the Next Test

The strongest Q2 MacBook Neo shipments were recorded while most customers could still buy the computer at its introductory price.

Apple raised the U.S. starting price to $699 in late June as memory and storage costs affected the Mac and iPad lineups. The education price remained at $599, preserving the original offer for eligible students and educators.

A $100 increase changes the comparison with discounted Windows notebooks and places the entry model closer to older MacBook Air configurations available through third-party retailers.

Apple must now determine whether the appeal came primarily from the low launch price or from the combination of macOS, battery life, design and iPhone integration.

The company remains better positioned than many rivals to absorb supply disruption, negotiate long-term component agreements and promote several notebooks at different prices. MacBook Neo also uses an iPhone-class A18 Pro processor rather than an M-series chip, providing another way to manage silicon costs and production capacity.

IDC expects the full PC market to deteriorate during the second half of 2026. Apple will enter that period with almost 10% worldwide share, but the September quarter will be the first to measure MacBook Neo demand almost entirely at the higher $699 retail price.

The post MacBook Neo Defies the Global PC Slump appeared first on AppleMagazine.

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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.

The awards are bestowed only once every four years to mathematicians under the age of 40.

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© Photograph: PA Images/Alamy

© Photograph: PA Images/Alamy

© Photograph: PA Images/Alamy

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