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Trezor warned customers on Wednesday that threat actors who breached its third-party email provider are targeting them in phishing attacks. [...]

Brněnský manažer Jiří Tobola podruhé prodal zahraničnímu kupci vlastní firmu, tentokrát Logmanager dánské skupině Guardsix. • Tobola dovedl Logmanager k obratu 130 milionů korun a spoléhal spíš na tradiční B2B prodej než na rizikový kapitál. • Za úspěchem stojí i brněnský inovační ekosystém ...

Praha prověří možnost vybudování 22 kilometrů dlouhé okružní linky metra E • Plánované spojení propojí okrajové čtvrti a uleví přeplněnému centru metropole • Finální podobu i vysoké náklady projektu posoudí studie proveditelnosti

Navzdory tomu, že Intel dlouhodobě a opakovaně hovořil o vydání Nova Lake v roce 2026 a toto stanovisko nikdy nedementoval, se podle informace od partnerů Intelu s letošním vydáním nepočítá…

AI is evolving faster than most people, even those building it, could even fathom, and its impact on the workforce and the economy is, at this point, really anyone’s guess.

Researchers from The Anthropic Institute are offering a few possibilities: They have built a nuanced framework looking at how AI might impact jobs, unemployment, and gross domestic product (GDP) growth between now and 2030.

They posit three potential scenarios for an AI-augmented future: “modest,” “substantial,” and “extreme,” and have created an interactive tool where users can explore how productive, or disruptive, AI will become in the workplace, based on their predictions of how they will work in 2030.

“Which of these worlds we are heading toward may become clearer within a year or two, and preparing for potential disruption seems to us the prudent course,” the researchers noted.

The goal of their work is to inform debate as AI becomes more powerful and capable. “AI is likely to reshape the US and global economies in profound ways in the coming decade, but how, and by how much, is extraordinarily uncertain,” they wrote.

How different scenarios could play out

If you add up every single task performed by people, machines, and software, the US has created a staggering $30 trillion in value over just the last year, the Anthropic researchers estimated. Their model and the corresponding tool are a way to explore how AI impacts tasks that contribute to the economy, the tasks it augments and creates, impacts on productivity, and speed of adoption.

“The answers to these questions have direct effects on GDP, the labor market, and the share of the pie taken home by workers,” they wrote.

Under their definition of “modest” change, AI will add less than half a point to GDP by 2030, meaning it will increase the growth rate of the national economy by just 0.5%, and will raise unemployment by just a tenth of a point, a minor shift. In this future, it’s difficult to see AI’s impact in macroeconomic data; change is steady but gradual, similar to that of the internet. “It drives real economic gains, but they’re within the historical norm for new technologies,” the researchers noted.

In the “substantial” scenario, AI will be capable of doing half of all knowledge work by 2030, the majority of it autonomously. Still, it wouldn’t be adopted for all work; in fact, most knowledge work tasks would still be completed without AI. Correspondingly, the economy would grow at twice its normal rate, but even as some non-knowledge workers see gains, wages for knowledge workers wouldn’t rise.

In this case, “AI makes a bigger impact than the internet, or the railroad,” the researchers wrote. Reallocation could be costly, but it is in line with what the US labor market has historically absorbed.

In the “extreme” scenario, of course, AI would be more productive than humans on the majority of knowledge work tasks, would do all of them autonomously, and subsequently would create no new knowledge tasks for humans.

The technology would “drive a completely transformed, unprecedented economy” arising from recursively self-improving AI. GDP growth would rise to 15% per year, but nearly one in five cognitive workers would be unemployed, and their relative wage would fall “immensely.”

The conundrum is that resources to compensate unemployed or under-paid workers will exist, but it’s unclear whether they would be fairly allocated. Mechanisms by which people can benefit from a much richer economy (retraining, income support, or universal basic income, for example) would become a question of economic policy.

“Whether and how those resources reach the people who bear the cost is not something growth delivers by itself,” the researchers wrote.

What users think

As well as developing the framework, the Anthropic researchers conducted a survey among roughly 11,000 Americans, asking them to predict AI use, productivity gains, automation versus augmentation, and displaced work.

They found that, in the main, public expectations land around the “substantial” scenario. That is, GDP would be 10% higher by 2030 than it would be without AI, and the overall unemployment rate would rise to around 5%.

Roughly 10% of respondents, on the other hand, had views in line with the “extreme” scenario.

Anyone can generate their own forecast using the researchers’ interactive tool, answering questions like: “Out of every 100 instances of a task AI can do in 2030, how many will AI actually be doing?”, “How many will be fully automated?”, or  “How much more gets done in an hour in 2030, compared with doing the tasks without AI?” The tool then responds, mapping their predictions to one of the three scenarios.

“Ultimately, what the economy looks like in 2030 depends on many factors, like what AI can do, and how companies and workers choose to adopt it,” the researchers wrote. “It also depends on how the financial benefit of this technology is shared.”

The between-the-lines reality

Sanchit Vir Gogia, chief analyst at Greyhound Research, emphasized that the Anthropic research “maps the conditions under which very different futures appear, it does not schedule destiny.”

He sees the distribution result, rather than the unemployment result, as the serious finding. In the extreme case, GDP is 32.4% above the no AI path, and the cognitive wage bill is 31% below it. Labor’s share of income falls from 60% to 45.2%, and capital income rises 81.4 %. That means a full 15% of GDP is captured as ROI rather than being paid out in labor costs.

In other words, he pointed out: “A richer economy is not automatically a fairer one.” Capability, diffusion, productivity, automation, and occupational friction all have to arrive together.

“AI will touch a large and rising share of knowledge work and will execute a much smaller share under independent authority,” he said. There is no single honest adoption percentage, because worker use, company use, technical exposure, and executed task instances are four different measurements.

Lessons from the research

Enterprises can take important lessons from the research as they deploy AI and consider its impact on their systems, workflows, and workforce, Gogia said.

“For enterprises, the binding variable is permission to delegate,” he noted. “A model that can draft a payment instruction is not thereby permitted to move money.”

His firm identifies five recurring concerns that come up in enterprise conversations: Durable returns after the full cost of deployment, control over authority being granted, augmentation quietly becoming substitution, erosion of professional formation, and fairness of how gains and risks land.

Some of those changes are progressing faster than the governance around them, he observed. Once a system can inspect customer data, change configurations, or act on workforce records, autonomy has stopped being a feature and has instead become an allocation of institutional authority.

“And the tasks easiest to automate are frequently the tasks through which judgement is learned,” he noted.

Open-source hra Shattered Pixel Dungeon (Wikipedie) byla vydána ve verzi 4.0.0. Přehled novinek v příspěvku na blogu.

Gartner on Wednesday said that it expects 30% of the positions eliminated by AI-related layoffs to be refilled by 2029, suggesting that the initial terminations were ill-advised and excessive.

“When business and IT executives look back on the early AI era, they will realize their greatest mistake was believing that work automation was the point, when workforce amplification was the opportunity,” said Tori Paulman, VP analyst at Gartner. “The competitive advantage will go to the CIOs and business executives who build an AI-shaped organization where AI value compounds by reshaping roles and allowing workflows to cross traditional boundaries, increasing velocity and reducing friction.”  

The Gartner report noted that it is finding that the cuts “deplete talent pipelines and erode institutional knowledge.” Beyond the immediate workforce disruptions associated with any mass layoff, companies will also face steep increases in costs for recruitment, training, and onboarding.

It also predicted that, by 2027, “75% of organizations that prioritize capturing AI productivity gains as cost savings will be eclipsed by competitors that aggressively reinvest those gains into innovation, modernization and upskilling.”

In an interview with Computerworld, Paulman said that the 30% figure represents the average impact on organizations of all sizes; they estimate that the layoff boomerang for enterprises would be even higher, roughly 40%. 

Paulman said that Gartner’s research found a lot of what they called “AI washing” by executives who want/need to do layoffs for purely budgetary reasons, and will falsely blame AI for the reductions because it makes them look better.

“More than 50% of our enterprise clients have been given a number [by their bosses],” Paulman said, and have been told by senior management to find that percentage of savings from AI.

But despite widespread evidence of problems due to AI-related layoffs, such job cuts are still increasing. 

Layoffs were ‘excessive’

Other analysts and consultants agreed with the Gartner suggestion that many of these job losses attributed to AI are going to be walked back, but questioned the specific statistic. Some also noted that 70% of the AI-attributed layoffs may remain in force, which would suggest that the original terminations were mostly justified. 

However, Frank Dickson, principal analyst at Dickson Research, argued that a lot of the layoff reversals will occur in a variety of ways that will obscure the fact that they are restoring a terminated role. 

“A lot of that 70% never shows up as a clean rehire even when the original cut was wrong,” he said, pointing out that some of the losses caused service to quietly get worse, and stay poor, some of the work was contracted out or offshored, some of the roles were reconstituted with a different position or title, and some was covered by the remaining staff absorbing the load. This,” he noted, “shows up later as burnout and attrition, not as a line item on this report. None of that gets counted in the 30%, and none of it is evidence the original call was sound.”

Melody Brue, principal analyst for Moor Insights & Strategy, added that the 70% scenario “could show that a substantial share of the AI-related workforce reductions is durable,” but, she stressed, “it shouldn’t be mistaken for endorsement of how those layoffs were made. What it doesn’t show is whether the organization captured the full economic value it expected. A lower headcount is not by itself evidence of a successful AI transformation.”

Valence Howden, advisory fellow at Info-Tech Research Group, questioned the methodology behind the calculation of Gartner’s 30% figure, but he agreed with the overall sentiment that layoffs attributed to AI have been excessive.

“I’m not sure we can substantiate those numbers, since it’s much more of a guesswork statement than anything else,” he said. “I do believe the current trend is going to lead to rehiring, especially as AI governance requirements ramp up and given AI’s lack of contextual semantic understanding. We know AI has not provided the value proposition that it has been sold as providing, and unless costs are controlled, it will be cheaper to use humans to perform some of the advanced work.”

Supporting data

Dickson also raised questions about the Gartner report because it lacked comparative layoff statistics. 

“Gartner doesn’t say what the reversal rate looks like for ordinary layoffs, the ones that have nothing to do with AI,” he said. “Suppose normal cuts get walked back at 10% to 15% in a typical five-year window, which is plausible given ordinary churn and business-cycle rehiring. A 30% rate specific to AI-driven layoffs would still run well above that, and that’s a damning number. Without that comparison, 30% is just a figure floating with no anchor.”

However, Dickson pointed to various datapoints supporting the position that AI layoffs have been excessive, noting that Forrester reported that 55% of businesses “already regret AI-driven cuts and are predicting half of those layoffs get quietly reversed.” 

“Robert Half puts it at a third of hiring executives who eliminated roles for AI having already rehired. Ford, IBM, Booz Allen Hamilton, Alphabet and CSX have all walked back cuts or announced rehiring drives,” Dickson said. “Gartner’s 30% by 2029 sits comfortably inside that range.” Klarna has also walked back AI layoffs. 

A ‘major indictment’

He added that many AI layoffs amounted to a corporate version of a crash diet. “You cut fast, you look great on the next earnings call, and eighteen months later, the weight is back, plus interest, because nobody fixed why the cut was made in the first place.”

Gartner’s Paulman agreed, noting, “business and IT executives who use AI primarily as a tool for cost cutting risk making reductions that are too deep and too soon, affecting their ability to innovate their business model and compete in new markets as AI continues to mature.”

Mike Wilkes, enterprise CISO at Aikido Security, said that even if the 30% figure turns out to be accurate, it is a major indictment of the layoffs. 

“If 30% of AI-driven layoffs must be reversed, that is an enormous error rate for a strategic workforce decision,” Wilkes said. “Imagine any other major capital decision where nearly one-third had to be unwound at a premium three years later. No CFO would call that a strong outcome.”

Amid industry soul-searching¹ about the possibility of AI improving itself to the point that it kills everyone, Anthropic has revealed yet another incident that would qualify as a crime if perpetrated by a person. The AI biz published "an alignment assessment" detailing four times Claude models accessed third-party systems without authorization. The company has already reported three of the incidents. Evidence of the fourth was lurking in a session transcript dating back to January 2026 when the misbehavior occurred. Anthropic found the first three by scanning around 141,000 transcripts where Claude could have obtained internet access during evaluation. It missed the fourth initially because "our scan relied on an agentic search." Felony Bench, a tongue-in-cheek record of cyber intrusions carried out by major AI companies without consequences, has added this newly-discovered incident to its rap sheet of rogue AI actions. The January 2026 AI trespass involved an early version of Claude Opus 4.6, which was given a Capture the Flag (CTF) challenge under the oversight of the third-party model evaluator where the other hacking events occurred. Opus 4.6 managed to sabotage its chances of success by disabling the machine it was targeting. It assigned the device an IP address that already existed on another piece of hardware, rendering the target unreachable and making it impossible to solve the challenge. Those familiar with other incidents where AI models violated third-party systems may recall that unsolvable tasks represent a common catalyst for misbehavior. Models exhaust all aligned options, and then turn to transgressive approaches. Opus 4.6 might have been an exception, but when it tried to abort the task after recognizing that it could not reach the target machine, it failed to do so "due to a misconfiguration in [the model's] evaluation harness." It failed to shut down not just once but seven times. So it continued onward, trying other expected means to reach the target machine but failing. Then it explored further. "The model discovered a machine belonging to a third party that it was able to access, and stated that it believed this third party was part of the CTF," Anthropic explained in its post. "Inside the machine, the model found a file listing a password, which it used to gain admin access to the system." The model went on to gather more credentials, and modified a system setting to make it easier to access the personal information of an individual associated with the third party evaluation organization. Opus 4.6 might have done more but for the fact that it exhausted its token budget, bringing the session to an end. Anthropic says it's not as concerned about this incident as the others because the model tried to abort its task. "While the model’s disregard for the possibility that it might be harming real systems or people is concerning, many of the behaviors described here have changed considerably as our training has evolved across model generations," the company said. Anthropic said it considers these incidents serious but expects current training approaches "are likely able to address the specific alignment failure modes observed in these incidents." And if company training methods fall short, there's no real consequence to anyone at Anthropic other than writing up a revised alignment assessment. ® ¹ The term "soul-searching" is figurative and is not intended to indicate a belief that the technology industry has a soul.

At least four espionage groups, most with suspected links to China, are using a new exploit kit that chains two Chromium-based browser flaws and one Microsoft Windows bug to break into organizations' networks in the US and Southeast Asia. Mark Kelly, a threat researcher at email security shop Proofpoint, told The Register that the researchers don't know exactly who was targeted, nor how, and so far the damage appears limited. “In terms of organizations targeted, we saw fewer than 20 organizations globally targeted across the activity highlighted," he said. "However, the true number is almost certainly higher than this.” Proofpoint’s threat hunters spotted the new kit, which they named BlueMoon, and said its first observed use started on August 28. This is when a Beijing-backed crew they track as TA412, also known as Violet Typhoon and APT31, used BlueMoon to “repeatedly” target non-governmental organizations (NGOs), mining companies, and physical commodity trading firms in the US. TA412 is a cyberespionage group linked by US authorities to China's Ministry of State Security (MSS), and American prosecutors previously charged seven alleged members with conspiracy to commit computer intrusions and wire fraud, alleging they broke into computer networks, email accounts, and cloud storage belonging to numerous critical infrastructure organizations, companies, and individuals. Just days after Proofpoint documented the late-August activity, “several other espionage-motivated clusters began using BlueMoon, the majority of which have a suspected China nexus,” Kelly and fellow researchers Greg Lesnewich, Konstantin Klinger, Saher Naumaan, Julia Paluch, David Galazin, and Stuart Del Caliz said on Wednesday, noting that there may be other, non-China-nexus attackers using the exploit kit as well. “BlueMoon was developed and deployed rapidly, and shared across multiple threat actors within days,” Kelly told The Register. “This may reflect a reduced cost and barrier to entry for this class of capability, which has historically been rare and high value, as AI agents increasingly enable threat actor exploit development. That is particularly true for open-source codebases such as Chromium, where publicly accessible upstream patches create a ‘patch-gap’ window for rapid reverse engineering and exploit development ahead of downstream stable releases.” A Google spokesperson declined to comment beyond what Proofpoint wrote. Microsoft patched the Windows bug (CVE-2026-85880) on Tuesday, and a spokesperson reiterated that customers who applied that patch are protected. BlueMoon attack chain The kit chains together three vulnerabilities. The first is a V8 type confusion (CVE-2026-85046) flaw that allows remote code execution and affects all Chromium-based browsers, including Google Chrome and Microsoft Edge. Google patched this bug in Chrome on September 3, and at the time warned that it “is aware that an exploit for CVE-2026-85046 exists in the wild.” Microsoft published a security advisory saying it fixed the flaw in Edge Stable version 152.0.4191.62 on September 2. The second is a Chrome V8 sandbox escape. This one also affected all Chromium-based browsers. It does not have a CVE because Google doesn’t issue them for sandbox escapes. Finally, the third bug is a privilege escalation vulnerability in Windows Advanced Local Procedure Call (CVE-2026-85880) that Microsoft patched on Tuesday, as noted above. Redmond also warned that this flaw had been exploited as a zero-day prior to the security update. The Proofpoint researchers also note that both V8 vulnerabilities are what’s called "patch-gap" zero-days at the time of the observed activity. This means they were known and fixed in upstream Chromium source code – a change containing the fix for CVE-2026-85046 was committed on August 7. But they remained unpatched in the latest stable releases of Chrome and Chromium-based browsers available to the public for weeks. “It is likely that the exploit kit developer used these publicly available Chromium patches to weaponize the browser exploit chain,” the researchers note. From phishing to browser surveillance The attacks start with a phishing email that tricks victims into clicking on an actor-controlled URL. This triggers the two V8 bugs to allow remote code execution and escape the browser sandbox. The attack chain then exploits the Windows bug to download multiple payloads including browser-surveillance malware, credential-stealing backdoors, and others, depending on the group using the exploit kit. TA412’s first campaign, which began on August 28, used a range of lures. Some of the emails purported to come from university students interested in internships at the targeted organizations, and some were more target-specific exchanges, intended to build trust with the individual before ultimately sending a malicious link via email. In these instances, the exploit chain “ultimately downloaded and ran a loader executable on the infected host, which then installed a malicious browser extension disguised as Google Gemini on the victim's Chromium-based browser,” the team wrote. This browser extension, which Proofpoint tracks as GemStone, allowed the Beijing spies to issue commands through a command-and-control (C&C) channel, steal cookies and other sensitive data, take screenshots, and inject a keylogger into a browser tab. The malware also contains a keyword monitor, which injects an attacker-specified keyword list into the top frame of each page, scans the HTML body for these keywords, and triggers a screenshot if it finds any. A few days later, beginning on September 2, a second China-aligned spy crew that Proofpoint tracks under the temporary group designator UNK_LateNight used BlueMoon to target multiple US aerospace companies. The phishing emails used request-for-quotation lures specific to defense industry organizations, and included links to attacker-controlled domains spoofing a variety of US aerospace companies. These websites also served the BlueMoon exploit kit and ultimately loaded a backdoor called ShadowPad, which has been shared among multiple China-aligned groups since 2019. Around this same time, on September 2, another suspected espionage group that Proofpoint tracks as UNK_DoubleCheck targeted a Vietnamese manufacturing firm with messages sent from a compromised Southeast Asian government email address. The fourth campaign began a day later, and involved suspected China-linked spy crew UNK_QuietRacket using BlueMoon to target government, consulting, and financial-sector organizations in Indonesia and Singapore. These phishing emails used lures related to Indonesian conferences, such as the Indo Startup Expo and Forum 2026 and the World Conference on Creative Economy (WCCE 2026). Proofpoint warns that BlueMoon will likely be used by both cyberspies and financially motivated attackers. “The broader dynamic revealed by this activity - rapid exploit development that leverages the open source patch-gap – is likely to recur beyond BlueMoon as this development model becomes accessible,” the team wrote. ®

Přehled srpnových vydání Jaderných novin: stav vydání jádra, citáty týdne a seznam článků týkajících se jádra.

Abyste dosáhli na co nejvyšší důchod, musíte správně načasovat odchod do něj. Na přelomu roku to platí dvojnásob. Rozdíl přitom může jít i do tisíců za jeden měsíc.

Ještě nedávno stačilo zmáčknout spoušť a poslat fotku do světa. Dnes vám ale u snímku může vyskočit „Made with AI“ či „AI info“, i když jste jen fotili. Na vině je C2PA a jeho průkaz původu obsahu.

Profesionální aplikace pro osmibitové počítače Atari byly většinou vytvářeny v assembleru. Existoval však jeden vyšší programovací jazyk, který byl dostatečně výkonný i pro tvorbu her: Action!

…aneb Fantastický nález ze Severní Dakoty

XPeng spustil výrobní linky na výrobu svých humanoidů IRON, velmi lidských robotů s úctyhodným výpočetním výkonem. Sledovat humanoidní roboty odcházející po svých z výroby musí být fascinujícím zážitkem. Na trhu by se měl IRON objevit už v roce 2027. Tesla se svým naslibovaným Optimem pláče někde v koutku.

Uživatel se rozhodl reklamovat GeForce RTX 4090, která se přehřívala a v důsledku toho systém zamrzal. Jenže místo výslovně žádané opravy / výměny dostal zpět peníze s tím, že kartu už neuvidí…

Cisco has confirmed that a maximum-severity authentication bypass vulnerability tracked as CVE-2026-20079 in its Secure Firewall Management Center (FMC) software is being actively exploited in attacks. [...]

Healthcare company AdaptHealth has confirmed that data of 4.1 million people was exposed in a cyberattack discovered in July that was attributed to the ShinyHunters threat group. [...]

Twenty-five years ago, I sat near the front of the room while then-Apple CEO Steve Jobs rolled out Apple’s digital hub strategy, a vision in which the Mac would become the central manager of all manner of portable digital devices.

Now, a quarter of a century later, nearly all of those digital devices have become features on your iPhone, and the arrival of AI means the iPhone has become the intelligent personal hub, the central manager of, well, of you. Apple’s new CEO, John Ternus, went straight for the concept in his important opening remarks at Wednesday’s big iPhone launch, where nearly all the pre-event speculation came true.

The intelligent personal hub

“As we look ahead, we see a future filled with enormous discovery and transformation,” Ternus said at the Surprise and Shine event. “AI makes entirely new kinds of experiences possible. An AI can become even more useful when it brings together your life with the apps, services, and products you use every day. And this means the product at the center of your experiences becomes even more essential — what I like to think of as an intelligent personal hub.”

(To be clear: He’s talking about the iPhone.)

Think about it: all those tasks you now do on your iPhone are tasks AI can help you with. Siri AI lets you work on files and folders using spoken commands, while contextual AI lets you sift through all the data once gathered by your iPhone to do things, make things, create networks, or even organize luncheon. 

“It’s the intersection of broad new capability, and a deep understanding of your personal context that makes this idea so powerful,” Ternus explained.

AI’s brave new world

That’s the context within which Apple will be introducing all its future products, a battle the company now feels confident marching into now that it appears to have resolved its historical challenges with AI. At the same time, the company also seems to be doing its best to lean into privacy — enabling life-changing AI transformation of daily lives while putting a brake on scary privacy attacks. This is going to be foundational to what happens next at Apple.

Of course, you’re not reading this to look too deeply into what Ternus said about the future of Apple; you’ll also want some insight into the slew of devices the company introduced at its event. That list includes the:

• iPhone Duo — the long-anticipated folding iPhone, which is finally here (or rather, will be here in October).
• iPhone 18 Pro and Pro Max — the next important evolutions of what was once Apple’s highest-end iPhone range.
• AirPods 5 — now with even better tech, at the same price as before.
• Apple Watch Series 12 and Apple Watch Ultra 4 — the latest iterations of the company’s popular wearables.

The big surprise? Price

For some, the biggest surprise in these introductions was price. All of the accessories were kept at the same price as last year’s equivalents, while the new Pro iPhones came in at only $100 more for the entry-level model, rising to around $300 more at the more price-resistant highest end. An iPhone 18 Pro Max equipped with the max 2TB storage comes in at $2,499 compared to the $1,199 iPhone 18 Pro entry point. 

When it came to price, though, all eyes were certainly on the iPhone Duo, and Apple has given everyone (except competitors) a pleasant surprise on that. 

With a starting price of $1,999, the folding phone’s price hit the low end of expectations, and while that grows to an eye-watering $3,199 with 2TB of storage, it compares well with the market leader, Samsung. While the Galaxy Z Fold 8 begins at $1,899, in terms of features and design, Apple’s folding phone lines up with the Galaxy Z Fold 8 Ultra, which costs from $2,099. 

Dig a little deeper and you’ll see that while the iPhone has IP68-rated dust and water resistance, the Samsung equivalent has only IP48. You can drop the Fold 8 into five feet of water for up to half an hour and it should be OK, but you can chuck your iPhone Duo in almost twenty feet of water, and it should survive. Though you probably shouldn’t test that unless you’re a hugely successful “influencer” already generating a small nation state of money through clicks. If that’s you, good for you. I’m only slightly envious. 

A productivity powerhouse?

What that suggests is that the iPhone is less likely to get damaged and is better engineered, though it’s also ever so slightly thicker. Apple’s device is powered by what the company modestly described as the most powerful mobile processor on the planet — which it is, particularly for the AI tasks all the new iPhones can handle. That matters, particularly for applied contextual AI deployed across daily lives.

It also matters because the combination turns Apple’s device into a mobile productivity powerhouse, one that I think will likely affect iPad sales more than anything else. Though this wouldn’t be the first time a new Apple release ended up eating its siblings. 

The productive possibilities of Apple’s premium device will also be realized as app developers embrace both the form factor and Siri AI. Apple said during the keynote that more than 300,000 apps already work with Siri AI and more than 2.5 billion Siri requests are being made each day. That latter number will only grow now that Siri performance has been dramatically improved. 

While this argument applies to all Apple’s new devices, the fact of the matter is that you now have an iPad-sized object you can carry in your pocket that is capable of doing some really powerful tasks by spoken command alone. That’s the iPhone Duo – a product of an unexplored territory at the crossroads between mobile computing, daily digital existence, and the opportunities of AI.

It’s not for everyone, yet

I don’t expect the Duo will be for everyone; the vast majority of users won’t be spending $2,000 on a phone. But we know that the cost of technologies like this tends to decline, which suggests that in perhaps five years’ time, it will be possible to create equivalent devices for a much lower price. Right now, it’s for affluent Apple consumers, C-class executives, students, AI pros, medical professionals and others who might actually need this kind of performance and display space to get things done.

This, incidentally, is precisely the same group of people that first embraced the iPad when it appeared, turning that into the world’s best-selling tablet.

What might this mean for Apple? 

I traded a few messages with IDC analyst Francisco Jeronimo, who sees it like this: “Apple entered the foldable market and, in one keynote, set the price and the standard every rival will now be measured against.”

He also pointed out, the iPhone Duo is “…a direct test of whether Apple can use design, ecosystem integration and premium positioning to reshape a segment that has so far remained selective.” 

He’s right, of course, but Ternus also just defined what that segment, and the industry, is driving for. I’s the same thing Apple was aiming at 25 years ago. “iPhone sits at the center of an amazing ecosystem of intelligent features and experiences that work seamlessly across the products you use every day,” the company said — an “intelligent personal hub.” And the Macs are all right, too.

Now please subscribe to my daily, human-curated Apple-related news headline feed at The Core, or follow me on BlueSky, LinkedIn, or Mastodon.

The Carnegie Mellon University CERT Coordination Center (CERT/CC) is warning that Skullcandy Dime 3 wireless earbuds accept Bluetooth pairing requests from nearby unpaired devices without requiring user interaction. [...]