Sonos will reveal its plans for conversational computing and predictive intelligence in September. The larger question is whether customers will receive genuinely useful features, greater privacy concerns, or another reason for hardware prices to rise.

Sonos has spent much of the past two years trying to convince customers that pressing Play should not require an act of faith. Now the company wants its speakers to understand the shape of your home, learn how your family lives and help usher conversational computing into every room.

Nothing could possibly go wrong.

During Sonos’ third-quarter fiscal 2026 earnings call, CEO Tom Conrad confirmed that the company will hold a product launch event in early September. Conrad did not reveal the product, its price or exactly what it will do, but said the event will introduce work centered on “conversational computing and predictive intelligence” inside the home.

This is not simply Sonos preparing another wireless speaker with a slightly larger woofer and a new shade of beige. The company is positioning artificial intelligence as the next major layer of its entire multi-room ecosystem.

Whether Sonos customers asked for that is another question.

What Is Sonos Planning?

Conrad argues that the industry is focusing too heavily on which company has the most powerful AI model. Sonos believes the more important advantage will be the hardware, software, microphones, speakers, services and household context surrounding that model.

According to Conrad, Sonos has already spent 20 years solving four difficult problems inside the home: form, sound, what he calls “systemness,” and intelligence. Its speakers already communicate across rooms, integrate with outside services and adjust their sound through technologies such as Trueplay. The next step is creating a system that can converse naturally and coordinate actions based on an understanding of the household.

Sonos also has scale. The company says more than 53 million connected Sonos devices are currently operating across more than 17 million homes. That installed base gives it something an AI startup cannot purchase overnight: millions of existing microphones, speakers and networked audio systems already sitting in kitchens, bedrooms and living rooms.

The company has not explained whether its new AI features will arrive through new hardware, existing products, a cloud service or some combination of all three. It also has not said whether customers will need a subscription.

Until September, any claim that Sonos is launching a particular kind of AI speaker remains speculation.

Sonos Has the Hardware but Trust Is the Problem

Sonos has legitimate advantages in multi-room synchronization, music-service integration, acoustic design and whole-home control. It also has a recent history that makes phrases such as “predictive intelligence” sound less comforting than management probably intends.

The disastrous 2024 app replacement removed features, disrupted existing systems and damaged confidence in a platform that customers had previously considered unusually reliable. Sonos has spent 2026 restoring functionality and improving navigation, volume control, speaker sorting and system stability.

Asking customers to trust Sonos with a more intimate understanding of their homes therefore requires more than an impressive September demonstration.

Sonos will need to explain what information its system collects, where that information is processed, how long it is retained and whether it is used to improve AI models. Customers also deserve to know whether conversations remain inside the home, which functions require the cloud and whether the intelligence can be disabled without limiting basic speaker operation.

Sonos Voice Control currently offers a useful precedent because its music and system commands are processed locally rather than sending voice recordings outside the home. A more ambitious assistant that remembers routines, coordinates services and predicts household behavior may require substantially more data.

Understanding that someone usually plays jazz in the kitchen at 7 a.m. could be convenient. Constructing a detailed behavioral map of everyone inside the house is something Sonos needs to explain before asking customers to applaud.

Sonos Is Not the Only Company Putting AI in the Living Room

Sonos is entering a market already being reshaped by Amazon, Google, Samsung and potentially OpenAI.

Amazon’s Alexa+ uses generative AI to provide more conversational responses, complete multi-step tasks and operate across compatible Echo, Echo Show and Fire TV products. Google has replaced Google Assistant with Gemini for Home on supported speakers and displays and now sells a $99.99 Google Home Speaker designed specifically around its AI platform.

Samsung is expanding its Vision AI Companion across its 2026 television lineup, offering conversational search, recommendations and contextual information based on what viewers are watching. OpenAI is also reportedly developing a screenless home device built around ChatGPT, microphones, cameras and environmental sensors.

Sonos is therefore not introducing AI to home entertainment. It is trying to convince customers that its version will be more useful because it is built around a mature audio system rather than added to a television, shopping platform or general-purpose chatbot.

That distinction matters, but this should not become another meaningless race over which assistant can produce the longest answer to a question nobody needed to ask their loudspeaker.

Consumers need AI to solve actual problems: finding music without memorizing exact titles, grouping rooms through ordinary speech, fixing connection issues, adjusting playback to suit different listeners and coordinating television, lighting and audio without requiring six apps and a minor in network engineering.

Otherwise, it is merely another feature consuming processing power while the speaker struggles to play the correct album.

AI Is Also Making the Hardware More Expensive

There is an uncomfortable irony surrounding Sonos’ AI announcement.

The same growth in artificial intelligence that manufacturers are using to sell smarter products is consuming enormous quantities of memory and semiconductor capacity, contributing to component shortages and higher hardware costs.

Sonos said rising memory prices reduced its third-quarter adjusted EBITDA by approximately $14 million. The company expects that headwind to reach $35 million during its fiscal fourth quarter and approximately $58 million for the full year. Sonos is attempting to reduce the amount of memory required by its operating system, secure component supply and decide whether pricing changes will eventually be necessary.

Conrad said Sonos has not materially raised prices on existing products in response to the shortage and wants to continue attracting new households. He also acknowledged that if elevated memory prices persist, the broader audio industry will probably move toward higher prices and Sonos will adapt with it.

This problem extends well beyond wireless speakers. IDC says AI data centers are consuming an increasing share of available DRAM and NAND production, while manufacturers shift capacity toward more profitable high-bandwidth memory used in AI infrastructure. That leaves less conventional memory available for consumer electronics and can result in higher prices, reduced specifications and product delays. Micron expects tight DRAM and NAND conditions to persist beyond calendar 2027.

Robert Silva is currently preparing a separate eCoustics report examining how the AI-driven chip and memory shortage could affect delivery times for audio and video equipment, which products are most exposed and how much more consumers may ultimately have to pay.

For Sonos customers, the concern is whether AI features arrive alongside more expensive hardware, subscription charges or both.

What Sonos Must Answer in September

Sonos needs to demonstrate more than a speaker carrying on a pleasant conversation under controlled lighting.

The company must explain which existing products will support its new intelligence, whether older speakers will be excluded and whether additional hardware will be required. It also needs to clarify local versus cloud processing, privacy controls, supported music and smart-home services, subscription requirements and whether customers can disable AI while retaining normal Sonos functionality.

Reliability matters even more than conversational polish. A system that can recommend dinner music but occasionally loses the living-room speakers is not intelligent. It is merely confident.

Sonos reported third-quarter revenue of $375 million, up 9% year over year, while adjusted EBITDA increased 24% to $44 million. Those results suggest the business is recovering and give the company room to invest in its next platform. They do not erase what customers experienced when the company last attempted a major software transformation.

The Bottom Line

Sonos may be better positioned than most audio manufacturers to make artificial intelligence useful inside the home. It already has the speakers, microphones, network, services and installed customer base required to turn voice requests into coordinated actions across multiple rooms.

That opportunity comes with significant risks.

Customers may be asked to accept greater data collection, increased dependence on cloud services, potential subscription fees and higher hardware prices at a time when many still remember the app collapse. The AI infrastructure boom driving this new strategy is also making memory more expensive, creating precisely the component pressure that Sonos says could eventually influence prices.

The September event therefore needs to answer a question larger than what Sonos is launching.

Can the company use AI to make its system simpler, more reliable and genuinely useful—or is it about to add another complicated layer to products that customers primarily want to play music?

Sonos does not need to prove that it can make a speaker talk.

After the past two years, it needs to prove that it can listen.

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If you have built anything with retrieval-augmented generation (RAG) in the last two years, you have lived its central frustration: You chop your documents into chunks, embed them, retrieve the top few that look similar to the question, and hand them to the model. For “What was our Q3 refund policy?” This works beautifully. For “What are the recurring themes across two years of customer complaints?” it falls flat — because no single chunk contains the answer.

The fashionable fix is GraphRAG: Instead of feeding the model isolated snippets, you first build a knowledge graph of the entities and relationships in your corpus, then use that structure as context. The pitch is seductive. But seductive pitches deserve scrutiny, so I went through the evidence — the original Microsoft paper plus four independent benchmark studies — to answer a simple question: When you swap text chunks for a context graph, do answers actually get better?

The short version: Yes, substantially — but only for the right kind of question, and not for free. Let me show you the receipts.

Why text chunks hit a wall

Standard vector RAG retrieves the k passages most similar to your query. That design has three structural blind spots:

• It can’t connect the dots. When an answer requires joining facts that live in different passages through a shared entity, chunks embedded in isolation never reveal the link.

• It’s blind to global questions. “What are the main themes?” needs the whole corpus, but similarity search only returns the handful of chunks that superficially resemble the question.

• It severs context at chunk boundaries. The relationships and hierarchy that complex reasoning depends on are exactly what chunking throws away.

Microsoft Research framed this crisply when they introduced GraphRAG: Baseline RAG “struggles to connect the dots” and performs poorly when asked to “holistically understand summarized semantic concepts over large data collections.”

What a context graph changes

GraphRAG attacks the problem before any question is asked. During indexing, a large language model (LLM) reads every chunk and extracts entities, relationships, and claims, assembling them into a weighted knowledge graph. It then runs community detection (the Leiden algorithm) to cluster the graph into a hierarchy of related topics, and pre-writes a natural-language summary for each community.

At query time, those summaries do the heavy lifting. Each relevant community drafts a partial answer (the “map” step), the partials are ranked and merged (the “reduce” step), and the model synthesizes a final response grounded in structure rather than in a few cherry-picked snippets. Variants like HippoRAG take a different route, using the graph plus a Personalized PageRank walk to find the right passages — but the core idea is the same: Let relationships, not just cosine similarity, decide what context the model sees.

The evidence: Four studies, one pattern

1. Global sense making: The headline win

Microsoft pitted GraphRAG head-to-head against naïve RAG on global, “make sense of the whole corpus” questions over million-token datasets, with an LLM acting as judge across three axes: Comprehensiveness, diversity, and empowerment.

GraphRAG won 72 to 83% of comprehensiveness comparisons and 62 to 82% of diversity comparisons against vector RAG. Its highest-level summaries used up to 97% fewer tokens than processing the source text directly.

That is not a rounding-error improvement. On exactly the kind of question that breaks text-chunk RAG, the graph wins two out of three times or better.

2. Multi-hop retrieval: The graph finds what chunks miss

The second piece of evidence is about retrieval quality: Does the right supporting passage even make it into the top results? On the standard multi-hop QA benchmarks (MuSiQue, HotpotQA, 2WikiMultiHopQA), graph-guided retrieval lifts Recall@5 dramatically:

• Average Recall@5 climbs from 73.4% (naïve RAG) to 87.8% (graph-guided), a +19.6 point gain.

• The biggest jumps come on the hardest, cross-document sets: +31 points on MuSiQue and +28 points on 2Wiki.

• HippoRAG reports up to a 20% accuracy improvement on multi-hop QA, at 10–20× lower cost and 6–13× faster than iterative retrieval methods.

3. The controlled head-to-head – where it gets honest

Here is where the story gains nuance. A 2025 study from Michigan State and Meta ran RAG against four GraphRAG families under one unified protocol — identical chunking, embeddings, and generation — and found no single winner. The two approaches are complementary:

• On single-hop, factual lookup (natural questions), plain RAG edged ahead (F1 64.8 vs. 63.0 for the best graph method).

• On multi-hop reasoning (MultiHop-RAG), graph-guided retrieval pulled in front (70.3 vs. 67.0 overall accuracy).

The lesson: A context graph is not a universal upgrade. It is a specialized one that pays off precisely when questions demand reasoning across pieces.

4. When to use graphs: The task-type verdict

The most recent benchmark, GraphRAG-Bench (ICLR 2026), set out to answer “In which scenarios do graph structures provide measurable benefits?” Its accuracy-by-task numbers map the boundary cleanly:

• Simple fact retrieval: Text chunks 60.9 vs. graph 60.1 — effectively a tie. The graph’s structure is overhead the query doesn’t need.

• Complex reasoning: Graph 53.4 vs. chunks 42.9 — a +10 point graph win.

• Contextual summarization: Graph 64.4 vs. chunks 51.3 — a +13 point graph win.

The scorecard

Read top to bottom, the pattern is unmistakable: The graph’s advantage grows with the reasoning depth of the question, while text chunks hold their ground on isolated facts.

The catch: Cost and the LLM-judge problem

Two caveats keep this from being a slam dunk, and ignoring them is how teams end up disappointed.

Building the graph is expensive. Having an LLM extract entities and relationships from an entire corpus isn’t cheap. One analysis put index construction at roughly $48 against GPT-4o for a moderate corpus, far above a vanilla vector index. (Microsoft’s own follow-up, LazyGraphRAG, defers extraction to query time and cuts that to around 0.1% of the cost – a tacit admission that the original budget is impractical for many deployments.)

Many of the wins are judged by another LLM — and LLM judges are biased. An independent audit found systematic flaws in this evaluation style: position bias (swapping which answer appears first can swing the win-rate by more than 30 points), length bias, and trial bias (identical comparisons disagree across runs). After correction, one popular method’s reported 66.7% win rate fell to about 39% — below the 50% break-even line.

The takeaway is not “the research is wrong.” It is that the large gains — the +20% multi-hop accuracy, the +15-to-30-point recall jumps — are robust, while narrow comprehensiveness margins deserve a skeptical second look with reference-based metrics.

So when should you reach for a context graph?

Strip away the hype and the decision is refreshingly practical.

Use a context graph when: Your questions are multi-hop, global, or sensemaking in nature; you need comprehensive, multi-perspective answers; and your corpus is richly interconnected (research libraries, case files, incident histories, knowledge bases).

Stick with text chunks when: Your queries are mostly single-fact lookups; your corpus is small or flat; and indexing cost, latency, and operational simplicity outweigh a marginal quality bump.

Best of all, go hybrid: The systematic studies converge on the same recommendation: route each query to the right method, or fuse evidence from both. Combining graph and chunk retrieval consistently beats either one alone. You don’t have to choose a religion; you have to build a router.

The bottom line

A context graph is not magic, and it is not snake oil. It is a targeted instrument. Hand it a question that requires connecting scattered facts or synthesizing a whole corpus, and it will outperform text chunks decisively. Hand it “what’s the phone number on page 3,” and you’ve paid for indexing you didn’t need.

The teams that win with GraphRAG in 2026 won’t be the ones who graph everything. They’ll be the ones who know which questions deserve a graph — and build pipelines smart enough to tell the difference.

Dattaraj Rao is an R&D architect at Persistent Systems

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The best way to answer this question is to look at the precedent Sony has already set. The PS4 originally launched in 2013, and to this day the console still semi-regularly receives firmware updates, with the most recent patch rolling out just last month. 

Granted, it amounted to little more than a few security fixes, but the fact that Sony still considers a 13-year-old console worth bothering about at all bodes well for the PS5. The PS4 also still functions just as well as it did when the PS5 replaced it in 2020, and despite unconfirmed reports last year that some of the console’s features would be sunsetted in 2026, you can turn on a PS4 today and expect it to work perfectly.

A new console generation replacing an old one almost always involves considerable software overlap for at least the first few years of the newer console’s life. In Sony’s case, this has been true since the very first PlayStation, with a number of PS1 titles still launching well into the PS2’s life, and it’s been the same story with every subsequent console the company has released.

Even today, the PS4 is still getting plenty of new games, even if the AAA side of things moved on a while ago. Incredibly popular games like Fortnite and Roblox still thrive on the last-gen Sony console, and it’s not unusual to see newly released indie games popping up on PS4 either at launch or a little while after.

Nothing lasts forever, though, and at some point those last remaining clingers-on will get a big old nudge towards newer hardware. Call of Duty: Warzone is being shut down on PS4 and Xbox One later this year, and it does feel like 2026 is something of a final twilight year for last-gen machines. And while it has published a few games on the PS4, the last notable first-party release on the console from Sony was probably 2022’s God of War: Ragnarok, which was the final big cross-generation game.

So what does this all mean for the PS5 and eventual PS6? It’s hard to predict Sony right now, as its first-party releases have dramatically decreased this generation. The longer development times and significantly higher cost of developing AAA games mean we haven’t had a single brand new game from Naughty Dog since the PS5 launched, and at this rate it wouldn’t be a huge shock if Intergalactic: The Heretic Prophet skipped the current generation altogether.

In all likelihood, the popularity of the PS5 means Sony will continue to support it in the same way it has done with all of its previous consoles, especially if the PS6 is as obscenely priced as some fear it could be. Just don’t expect any cross-generation releases to come on a disk, as Sony is ending support for physical games in 2027.

A few days ago, Apple introduced a new “Upgrade” program that is essentially a lease program for buying Apple hardware. Instead of paying the full cost up front, you space the monthly installments (12, 24, and 36 month spells), and at the end of the lease period, you can choose to return the device, upgrade to a new one, or pay the remainder cost and keep it forever.

The broad idea is simple. Instead of taking a thousand-dollar hit on the wallet, you space the hit across small monthly payments. For a flagship iPhone, that broadly comes down to a dollar per day, and for budget phones such as the iPhone 17e, it’s just half a dollar each day, if you do the breakdown. At least Apple is looking at it that way.

“It’s a way to get into a product on a fairly affordable basis, particularly for those customers who want to upgrade on some kind of schedule,” outgoing Apple chief Tim Cook said during the company’s recent earnings call. The timing of the launch is no coincidence.

Bloomberg says Apple has been working on the program for years, but only decided to launch it in 2026. You know what else is happening in 2026? A price hike for iPhones. As per analyst estimates, the iPhone 18 Pro duo could see a price hike that’s as generous as $300 for the flagship.

Soften the blow. And make upgrades less taxing.

Imagine paying close to $1400-1500 for an iPhone. And yes, the hikes are imminent, if Apple’s own comments are to be believed. Look no further than the price surge that was announced for the Mac and iPad hardware merely a few weeks ago. And oh, let’s not forget the elephant in the room — the foldable iPhone.

This one is going to cost north of $2,000, as per multiple reports, including reliable sources such as Bloomberg. Samsung already sells its latest Galaxy Z Fold 8 Ultra starting at $2,100, so it won’t be surprising to see Apple crossing that sticker price barrier.

I know half a dozen people in my close circle who own a foldable phone. None of them paid for it in one go. Forking close to two thousand dollars — on a phone — simply doesn’t make sense. Plus, a person who is willing to spend that amount on a phone is clearly a tech-savvy person, and they will most likely switch to a shiny new model in a year, or two.

That’s exactly what Apple is targeting. “If you look at the Apple Upgrade, what it’s all about is making it easier for customers to get their hands on our latest products with a leasing plan that’s right for them,” Cook said during the earnings call.

Apple is not only giving customers a more affordable (in the short-term, at least) option to own an iPhone, but also readying the field for its obviously premium foldable iPhone that arrives this fall.

Even in developing countries like India, a huge majority of iPhone users rely on local financing schemes to buy those devices. And considering the fact that Apple’s Upgrade program offers unlocked devices, the target audience will likely lap it up.

The saving grace.

I won’t mince words here. Leasing and owning are entirely different things. With the Upgrade program, Apple essentially wants to trap you in its walled garden. You don’t technically own the device. You either let Apple take it back at the end of the lease period, or you exchange it with a new iPhone. Or Mac. Or Apple Watch. Or iPad.

If you do maths on the back of a napkin, you aren’t really in for a rewarding deal unless you love tech upgrades but don’t want to pay hundreds of dollars each time you want to experience a new iPhone. But there is a third route that can actually be a winning proposition for many.

At the end of the lease period, you can just pay off the remainder amount, convert the lease into full ownership, and subsequently sell the device in the second-hand market. The benefit? The depreciation on iPhones is dramatically lower than that of any rival brand’s competing product out there.

Apple knows that too well, and even its CEO is mildly nudging the target audience to take this route. “Of course, as you know, the residual values on Apple products are generally much higher than the residual value on several of our competition,” Cook was quoted as saying during Apple’s Q2 earnings call.

It’s a reality I’ve witnessed year after year. And it applies not just to iPhones, but also Macs, iPads, and Apple Watches. Apple’s just making the best of a status quo it has built over decades, and it’s finally in a position to reap rewards that are simply not feasible for its rivals.

The downside of KeePassXC is that it doesn’t have official mobile clients. However, third-party apps are available for iOS and Android. KeePassXC is a fork of KeePass that offers better cross-platform support, but there are a handful of other forks available, as well.

Download the desktop app for Windows, macOS, or Linux and create your vault. There are also extensions for Firefox, Edge, and Chrome. The project does not offer apps for phones. Instead, it recommends KeePass2Android or Strongbox for iPhone.

Other Password Managers We’ve Tested

Password managers are not a one-size-fits-all solution. Our top picks cover most use cases and are the best choices for most people, but your needs may be different. Fortunately, there are plenty of good password managers out there. Here are some more we’ve tested.

Google Password Manager (Free): Google has offered a password manager within Chrome for years, but it recently broadened with a dedicated Android app. Although storing passwords in your browser has a few security concerns, Google offers top-notch encryption, the option to turn on on-device encryption, and integration with biometric authentication on Android and Windows. It can even store passkeys. For most people, I recommend a third-party password manager, but if you aren’t using a password manager at all, Google Password Manager is a good option.

RoboForm ($30 Per Year, $48 Per Year for a Five-User Family Plan): RoboForm has most of the same features as the rest on this list, but it lacks some of the things that differentiate our top picks, like Bitwarden’s open source aspect and 1Password’s travel features. I’ve been testing the free plan for a while and haven’t run into any problems. There are apps for every common platform, and it’s easy to use. RoboForm recently completed an independent security audit and came out looking good.

Pass (Free): Pass is a command-line wrapper around GPG (GNU Privacy Guard), which means it is only for the nerdiest users. It supports managing encrypted .gpg files in Git, and third-party mobile apps are available. It’s not for everyone. For years, this was my password manager of choice, but eventually, Bitwarden’s ease of use won me over.

Password Managers to Avoid

Most password managers are decent. Some are more secure than others, but the top password managers largely compete on features and pricing. Security is a prerequisite. However, there are a couple of password managers you should avoid for various reasons.

ExpressKeys by ExpressVPN: ExpressKeys is from ExpressVPN (formerly Keys by ExpressVPN that I tested, but it’s the same product), which ranks among the best VPN services on the market. But ExpressKeys doesn’t live up to the same standard. It’s secure, but I struggled to get it to work with any consistency. The browser extension requires the desktop ExpressVPN app to work, and closing either will force you to sign back in all over again. Worse, ExpressKeys wouldn’t recognize that I was signed into my ExpressVPN account about half the time. It needs some serious fixes before I can recommend it. —Jacob Roach

Google is preparing a new Chrome security feature that would block policy-installed extensions from hijacking the New Tab page or changing the default search engine.

BleepingComputer spotted the protection in a chain of work-in-progress Chromium Gerrit changes. It has not shipped yet, but Google plans to enable it by default once the changes are approved.

“In low-trust environments (unmanaged consumer devices), enterprise policy force-installs and recommendations are abused to lock in search engine or new tab page hijackers,” Anunoy Ghosh, who works at Google, wrote in a post.

“This CL enables the kBlockDseNtpOverrideExtensionsOnUnmanagedDevices feature flag by default, activating the end-to-end blocking defense on unmanaged Windows and macOS devices.”

Right now, Chrome allows organizations to use enterprise policies to force-install extensions and control browser settings.

It’s not exactly bad on properly managed work devices connected to a domain or mobile device management system, but malware has been abusing the same feature on regular consumer PCs.

A malicious program can add local Chrome policy keys without your permission and force-install an extension that replaces the New Tab page, changes your search engine, or redirects searches to suspicious websites.

Chrome may then believe that the extension was installed by an administrator, which prevents you from removing or disabling it.

In some cases, Chrome also displays the confusing “Managed by your organization” message, even though the PC is not actually owned or managed by an organization.

Google describes these consumer PCs as “low-trust” environments because Chrome is reading policies stored locally without confirmation from a trusted authority, such as a domain or MDM service.

Under the proposed protection, Chrome would block attempts to install policy-controlled extensions that override the New Tab page or default search engine.

The installation would be canceled, and Chrome would save the extension ID in a blocked-extension preference.

Chrome would also stop trying to download the same blocked extension during future policy checks, which should prevent repeated installation attempts and unnecessary network activity.

Google is also addressing another trick used by malware

An extension that you installed manually would no longer be converted into a locked, policy-controlled extension. It would remain under your control, so you could still disable or remove it.

If a previously managed device loses its trusted management status but still has local policy keys, Chrome would automatically uninstall affected New Tab and search-engine override extensions.

Google is adding metrics to measure how often these policy-based hijackers appear and how frequently Chrome blocks them.

Legitimate administrators would also have access to an escape-hatch policy that disables the protection when a required enterprise extension overrides the New Tab page or search engine.

The Gerrit changes are still under review, so the feature is not available in stable Chrome yet.

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The Malaysian government has ordered the shutdown of a startup-centric community called the Network School, according to The Wall Street Journal.

Billing itself as “a frontier community for techno-optimists,” the Network School reportedly grew out of entrepreneur and investor Balaji Srinivasan’s conviction that the United States is in irreversible decline; Srinivasan has said his goal is “to start a new country.”

So the Network School is supposed to represent the first step in a seven-stage plan (outlined in Srinivasan’s book “The Network State”) for building a new society. Based in an abandoned Malaysian hotel, the WSJ described the program as “part tech incubator, part self-improvement retreat.” Attendees, however, complained about moldy rooms, as well as a dearth of women and nightlife. 

After receiving questions from the WSJ, Srinivasan – who apparently renounced his American citizenship in 2023 — published a lengthy post on X declaring himself “a proud Singaporean” and complaining that the WSJ was working on a “hit piece” that would make him look like “an odd duck […] as opposed to an early adopter.”

And although the current Malaysian campus will reportedly need to shut down due to licensing issues, Srinivasan recently announced a new agreement for a campus in Kazakhstan.

Apple’s Mac sales rose by more than 25% in the last quarter, compared to the same time period in 2025, solely on the back of the MacBook Neo that incoming CEO John Ternus championed.

Shortly before John Ternus was announced as Apple’s next CEO, it was noticeable that he was the one championing the MacBook Neo instead of Tim Cook. As well as part of handing over the reigns, though, this was because the MacBook Neo project was driven by Ternus.

Now according to figures from Apple’s latest earnings call, the company’s Mac revenues climbed by 28.66% over the last quarter. This is mostly due to the only new product available just prior to the quarter, the MacBook Neo.

Mac quarterly revenue, as of Q3 2026

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Apple reports that during the quarter ended June 27, 2026, it sold $10.35B worth of Macs. That compares to $8.05B for the same quarter in 2025.

Significantly, though, there were no new Macs released during this quarter at all — but the MacBook Neo ramped up to full availability. The quarter also saw steep price hikes across the whole Mac lineup, including adding $100 to the price of the MacBook Neo.

Mac quarterly revenue change year-on-year, as of Q3 2026

The timing of that price rise won’t have been chance, though, as it was announced on June 25, 2026. That means that the MacBook Neo only saw the higher price for two days of the quarter.

Consequently there’s no indication of whether the MacBook Neo can continue this huge success in the next quarter. But it’s a measure of how the lower-cost MacBook Neo was an immediate boost to Apple’s bottom line.