Technology

• The iPhone Duo animation has been recreated on a Galaxy Z Fold 8
• For now, it's only a demo — not something you can set up yourself
• Samsung would need to implement the hack for it to work system-wide

The initial reactions to Apple's new, foldable iPhone Duo seem to be largely positive, with a lot of praise for the various software tweaks that have been implemented in iOS 27 for the new form factor — including a cool transition animation that shows up when you switch between the inner and outer screens.

As you open up or close the main 7.6-inch display, the wallpaper dissolves and seems to pass through the moving half of the screen to reappear on the other side. It's a neat effect, and one Android user has wasted no time in recreating the effect on their Samsung Galaxy Z Fold 8 foldable.

"Felt a bit of FOMO seeing that cool iPhone Duo animation Apple showed off today, so I poked around and found that the Z Fold 8 has a really accurate hinge sensor," explains the user in question on Reddit. "I realized it would be pretty simple to take the hinge sensor values and use them to drive a transition animation on both screens."

Tried to recreate the iPhone Duo animation on my Fold 8 from r/GalaxyFold

It works really nicely too, as you can see from the video embedded above. The half of the display that's opening and closing looks like it becomes semi-transparent as it moves, revealing what's underneath — though as with the iPhone Duo, it's all trickery involving motion sensors and gradients.

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♬ unstoppable - ATALE Over to you, Samsung

Unfortunately, this is only a demo for now — as in, an app that screenshots the display, then shows how an open/close animation could potentially work. It's not actually something you can apply at the system level and use across all your apps, and the developer says only Samsung would have the necessary access for that.

The reactions in the Reddit thread are overwhelmingly positive, nevertheless. The hack gets described at various points as "great", "awesome", and "so cool" — and it's genuinely impressive that it was created so soon after Apple's event. It seems a lot of Samsung foldable owners would want this on their phones.

There is perhaps a word of warning here for iPhone Duo buyers. When asked about using the effect, the original poster says "honestly, it looks cool for the first minute, but it gets really annoying after that", adding that "the effect is also only convincing head on" and "any difference in viewing angles really breaks the illusion".

Whether or not that's the case on Apple's foldable remains to be seen, but it's worth considering. For the time being at least, the iPhone Duo is the only folding handset that has this particular animation — and if you've really got to have it, pre-orders open on Friday, October 16, with prices starting at $1,999 / £1,999 / AU$3,499.

OpenAI launched ChatGPT Images 2.5 this week, boasting of impressive upgrades to the AI image creator. The company says its latest model delivers sharper details, more natural lighting, and richer textures, while doing a better job of preserving people and objects from reference photos. It is also supposed to generate images up to 50% faster and follow complex visual instructions more reliably.

There are plenty of genuinely useful additions around it, too. Images 2.5 introduces Sketch for turning rough drawings into finished images, templates for things such as posters and product photography, and the ability to leave comments directly on an image when requesting edits. OpenAI calls it its new state-of-the-art image model, which is a reasonably high bar to set for something that inevitably ends up being asked to draw dragons before breakfast.

The early reaction, though, is much less tidy. One Reddit user delivered perhaps the most memorable review so far: “The noise patterns are awful”. Another looked at an AI generation and decided, “This…looks terrible.” The complaint is easy to understand. It's all about the fuzz

Everything is sharper, including the problem

Noise is a slightly slippery criticism when talking about AI images. In photography, it usually means the speckling or grain that creeps into an image with a poor match between lighting and photographic technique. But generative AI images can produce something similar without ever encountering a camera. Everything gets a gritty, sandy look, and nothing looks clean for some reason.

Noise is especially damaging because it can masquerade as detail. Generative models have learned that photographs contain grain, texture, tiny variations in color, and the occasional imperfection. Images 2.5 appears unusually eager to reproduce those signals, sometimes scattering them across skies, skin, feathers, and studio backdrops.

That's the opposite of the “sharper details” and “richer textures” promised by OpenAI. Despite emphasizing more natural lighting and textures, some users think ChatGPT Images 2.5 has moved backward.

“They need to get rid of these noisy artifacts. It makes it mostly useless for production,” one wrote. There is praise mixed in with the complaints, but it's far from the universal acclaim OpenAI would likely prefer.

I wanted to see whether the problem survived outside Reddit screenshots, so I tested a few images with highly detailed subjects and areas that should remain visually smooth to see if any graininess crept in. I even made sure in my prompts to ask for smooth gradients and the elimination of any visual noise.

(Image credit: ChatGPT Image 2.5)

I started with a picture of a mug in a photo studio. The peacock, flowers, fruit, and landscape wrapped around the virtual ceramic are impressive at first. Look closer, however, and the grey studio background has a persistent granular texture, while some of the smallest patterns begin to dissolve into a mess. The model has generated a great deal of visual information without always deciding which parts deserve clarity.

A similar issue occurred when I asked for a barn owl at twilight. The feathers are a perfect excuse for complexity, and Images 2.5 handled them well. Individual structures remain visible across the wings and face, the talons are convincing, and the animal avoids the plasticky quality that older AI wildlife images often had.

Behind it is a sky that should have been the visual equivalent of a clean sheet of paper. Instead, the smooth blue area has obvious fine grain across it. It doesn't ruin the picture, but once I noticed it, I couldn't stop noticing it.

(Image credit: ChatGPT Image 2.5) Mythical grain

(Image credit: ChatGPT Image 2.5)

I thought a fantasy scene might fare better as the model wouldn't be relying on actual photographs of a winged horse or dragon. But while they coexist with a rainbow at an alpine lake in a surprisingly coherent composition, the issue is visible immediately. The relentless crispness gives the picture the air of a video game loading screen.

The pale-blue sky has a faint textured quality rather than the completely clean gradient I would expect from an ideal synthetic image, while the mountains, trees, and creature details have a crunchy, heavily sharpened look when examined closely. It is nowhere near a disaster, but the image feels like an overprocessed photograph.

I was very impressed with how my request for two friends on a rooftop in the evening came out. It came closest to selling the promised leap in realism, with convincing expressions and what seems like real gravity affecting their clothes. But if you look for more than a minute, the grain creeping across the twilight sky and their hair and skin is glaringly obvious. That's especially the case since there was no poorly set lighting or malfunctioning camera sensor. If the twilight sky looks grainy, the grain is a creative decision or model artifact rather than the unavoidable physics of taking a photograph in bad light.

(Image credit: ChatGPT Image 2.5)

Judging these four images specifically on image cleanliness, I have to side with the Reddit grumblers. There is a persistent fine texture that crops up in skies, studio backgrounds, and low-light areas. Sharper is an easy quality to advertise because it looks terrific in a launch gallery, but it may have been taken to excess, landing some of ChatGPT Images 2.5's results in the uncanny valley.

Images 2.5 is fast, adaptive, and often competent, but its fondness for granular texture weakens the realism it is supposed to embody. Perhaps the next upgrade will acknowledge that the most impressive thing an AI image generator can put in part of a picture is essentially nothing.

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• Lenovo offers up Eva Longoria to help solve your SMB woes
• Desperate Housewives star is an entrepreneur and serial investor herself
• Users can submit questions now, with Longoria answering on her Instagram page

If you're looking for that extra bit of business support and advice, you might seek out a mentor, someone who's been there and done it all - but how about a well-known TV and film icon?

Lenovo has teamed up with Desperate Housewives star Eva Longoria, who both owns her own business, and has invested in companies involved in everything from food and beverages to media, to offer her unique advice on the issues affecting fellow entrepreneurs.

Entrants will be able to submit their burning questions to the star via a special phone number or through CallEvaNow.com, with Longoria providing assistance at a later date.

Call Eva

The "Call Eva" idea is part of Lenovo’s global Backing Every Business program, which looks to "connect entrepreneurs across the world who are facing similar business challenges, creating peer-to-peer relationships where they can exchange experiences, perspectives and practical advice".

Entrepreneurs in Brazil, France, India, Japan, Mexico, the United Kingdom and the United States will be able to take part, with Longoria selecting the most pressing to answer on her personal Instagram feed on September 16, 2026.

“One of the biggest lessons I’ve learned as an entrepreneur is that you don’t have to have all the answers yourself. Some of the best decisions start with asking someone you trust for insights, a fresh perspective or even just reassurance that you’re heading in the right direction,” Longoria said

“That’s what I love about Lenovo’s Backing Every Business initiative and now Call Eva. We’re giving entrepreneurs a place to bring the questions they’re really wrestling with, because chances are, another business owner has faced similar dilemmas. If something I’ve learned along the way can help someone better navigate a challenge, make a tough decision or take their next step with more confidence, that’s incredibly meaningful to me.”

(Image credit: Lenovo)

“Technology can unlock enormous opportunities for small businesses, but technology alone doesn’t build a business. Entrepreneurs also need access to trusted advice, shared experiences and a community that understands the realities of building and growing a business,” said Emily Ketchen, Senior Vice President and Chief Marketing Officer, Intelligent Devices Group and International Markets, Lenovo.

“Backing Every Business brings those pieces together and Call Eva gives us a new avenue to extend that support while listening directly to entrepreneurs about the challenges they’re navigating. By understanding their questions, we can continue to find meaningful ways to help small businesses move forward and grow with confidence.”

Artificial intelligence is rapidly changing how software is designed, written and tested. Development teams can now use AI to generate code, produce test cases, identify likely defects and automate repetitive quality assurance (QA) tasks at a speed that would have seemed unrealistic only a few years ago.

That acceleration is valuable. But it also creates a new QA problem.

When the same class of technology is used both to create software and to decide whether that software is correct, organizations risk building a closed loop of confidence. An AI model may generate code based on a particular interpretation of a requirement, then generate tests based on the same interpretation. If the original assumption is wrong, both the code and the test can agree with each other while still failing the user.

AI, in other words, cannot be the sole judge of its own work.

This is not an argument against AI-assisted development. Errors, hallucinations and inconsistent outputs are expected features of a technology that is still maturing. The more important question is whether organizations have independent mechanisms capable of detecting those failures before they affect customers, employees or critical business processes.

Shared assumptions create shared blind spots

Traditional software assurance already recognizes the value of separation between development and testing. The people who build a system understand it deeply, but that familiarity can make it harder to challenge the assumptions on which it was built. Independent testers approach the same system from a different perspective, looking not only at what the software was intended to do but also at how it might fail.

The same principle applies to AI.

Models trained on similar data, prompted with the same requirements or operating within the same development environment may reproduce the same blind spots. A model generating a feature may overlook an ambiguous requirement, an unusual user journey or a device-specific edge case. A second model asked to test that feature may reinforce the omission rather than expose it.

This becomes particularly risky when AI-generated tests are treated as evidence of quality, simply because they run successfully. A passing test confirms only that the test’s conditions were met. It does not prove that those conditions were complete, independent or meaningful.

The result can be a technically consistent system that is practically wrong.

The most fundamental tension between generative AI and formal software assurance is repeatability. Modern AI coding agents are designed to generate and adapt. I.e. given an apparently identical objective, they may choose different steps, use different tools, interpret context differently and produce different code or tests.

This is not always because the system is learning during each run, it is also a consequence of probabilistic generation, changing context and evolving models. That variability can be highly useful when teams are exploring solutions, but it conflicts with the core discipline of QA. I.e. a controlled test must be capable of being re-run against the same version, in the same conditions, with defined expected results and evidence of a clear pass or failure.

Without that control, organizations may have AI activity rather than assurance, and outputs that look plausible, but cannot be reliably reproduced, measured, audited or defended.

Functional success is not user success

Many automated tests evaluate software through code-level signals. They check whether a service returns the expected response, whether a page contains a particular element, or whether a button can be located through an identifier or selector.

These checks are important, but they are not the same as validating the user experience. A test may confirm that a button exists even though it is hidden behind another element. It may verify that a field contains text without recognizing that the text is truncated, displayed in the wrong location or rendered in a way that makes it unreadable.

It may find a menu that is technically present but inaccessible on a smaller screen. It may confirm that a transaction completed while missing the fact that the confirmation shown to the user contains the wrong amount, account or status.

From the system’s perspective, the software may have behaved correctly. From the user’s perspective, it has failed.

This distinction matters because modern digital services increasingly depend on complex combinations of application code, browser behavior, operating systems, screen sizes, remote desktops, virtual environments and third-party components.

A change in any one of these layers can alter what appears on screen without necessarily causing a conventional functional test to fail. Testing must therefore examine not only what the underlying system reports, but what the user actually sees and can do.

Why visual validation matters

Visual user-interface validation provides an independent perspective because it tests the rendered outcome rather than relying solely on the application’s internal structure.

That independence is significant. Code-based tests often depend on knowledge of the system they are testing: object identifiers, document structures, accessibility labels, APIs or expected data responses. Visual validation can assess the final interface as presented to the user, including layout, positioning, content, state and usability across different environments.

Visual validation is not a separate phase of software assurance, nor a replacement for functional, integration, security, or performance testing. Instead, it applies across every assurance division wherever a user interface is designed, built, changed, or tested—from individual components and unit-level checks through integration, system testing, and user acceptance testing.

Functional testing confirms that an operation completed correctly; visual validation confirms that the result is displayed accurately, consistently, and remains usable. Reliable assurance requires both throughout the development lifecycle.

The need becomes more pronounced as AI generates a larger proportion of software changes. AI tools can produce code quickly, but speed increases the volume and frequency of change that quality teams must assess. Without an assurance layer focused on the rendered experience, defects can move through delivery pipelines faster than organizations can recognize them.

Visual validation acts as a check on the gap between technical execution and human experience.

Repeatability turns automation into evidence

AI is effective at generating ideas, scripts and possible test scenarios. Its outputs, however, can vary between runs. A model may interpret the same instruction differently depending on context, configuration or probabilistic variation. That flexibility can be useful during exploration, but it is not enough for formal assurance.

A test used to approve a software release must be repeatable. The same inputs should produce the same procedure, the same checkpoints and the same criteria for success or failure. Teams must be able to establish what was tested, when it was tested, which version of the application was involved and why the result was accepted.

AI is effective at generating ideas, scripts and possible test scenarios, but generative and agentic systems are not inherently deterministic controls. Their output can vary because of probabilistic generation, prompt and context changes, model updates, retrieval results and the decisions made as an agent selects tools and plans its next action. For software development, this flexibility can accelerate discovery. For formal assurance, it creates a material control problem.

A test used to approve a software release must be repeatable and auditable. The same application version, inputs and environment should produce the same defined procedure, checkpoints and success criteria, allowing teams to establish precisely what was tested, when it was tested, which version was involved and why the result was accepted.

Only then can passes and failures be measured over time, defects reproduced, and evidence relied upon in an audit or regulated setting.

This is the difference between using AI to accelerate test creation and allowing AI to become the test authority.

AI can help teams draft test cases, identify gaps and reduce the effort required to automate routine workflows. Once a test is adopted as part of an assurance process, however, it should become controlled, deterministic, traceable and auditable. Its expected results should be explicit. Changes should be reviewed. Failures should be reproducible. Evidence of passes and failures should be retained.

Without those controls, an organization may know that an AI system performed ‘some testing’ but be unable to demonstrate precisely what happened. That is a weak basis for operational confidence and an even weaker basis for accountability.

Regulated environments raise the stakes

The consequences of interface errors are not distributed evenly.

In a consumer application, a misaligned field or incorrect message may create frustration and lost revenue. In finance, healthcare, defense or government, a similar defect can influence a payment, clinical decision, operational instruction or public service. An interface that displays the wrong status, conceals a warning or presents outdated information can create consequences far beyond the screen itself.

Regulated organizations must also be able to explain and evidence their controls. It is not enough to claim that a system was tested. They may need to show that testing was consistent, that results were reviewed and that software behaved as expected in the environments where it was deployed.

AI-generated assurance that changes from one run to another makes that task harder. So does a testing strategy that concentrates on internal system responses while neglecting the final interface used by staff or customers.

Independent, repeatable visual validation can help provide a clearer chain of evidence. It shows not merely that an application returned the expected data, but that the right information appeared in the right place, in a usable form, at the point where a human decision or action was required.

This is particularly important when apparently minor presentation errors can alter behavior. A hidden warning, misplaced decimal point, incorrect unit or outdated status indicator may not prevent an application from functioning. It can still cause a user to take the wrong action.

In these environments, the interface is not simply a cosmetic layer. It is part of the operational control system.

Combining speed with control

The strongest approach is not to choose between AI and established quality disciplines. It is to assign each the role for which it is best suited.

AI can increase development speed, broaden test coverage and reduce the manual effort involved in producing automation. Independent validation can challenge the assumptions embedded in those outputs. Deterministic testing can convert useful AI-generated ideas into repeatable controls. Visual checks can confirm that technically successful software also works for the person in front of the screen.

This layered model allows organizations to benefit from AI without confusing productivity with proof.

It also recognizes that no single testing method can provide complete assurance. Code-level checks can confirm the behavior of individual components.

Integration tests can establish whether systems communicate correctly. Security testing can expose vulnerabilities. Performance testing can examine behavior under pressure. Visual validation, at all levels of UI development, can determine whether the final result remains accurate, accessible and usable.

The value comes from combining these methods, not asking one of them to stand in for all the others.

As AI becomes more deeply embedded in software delivery, assurance must become more independent rather than less. Organizations should assume that AI-generated software will sometimes be wrong, incomplete or unexpectedly inconsistent. The objective is not to eliminate every error at the point of creation. It is to make sure those errors are visible before they reach the user.

AI can help write the homework. It can even suggest how the homework should be checked. But the final mark must come from an assurance process that is independent, repeatable and accountable.

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Pixel Watch 5 leads overall, while Galaxy Watch9, OnePlus Watch 3, and Galaxy Watch Ultra2 offer better fits for Samsung users, battery life, or outdoor use.

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• One in three workers laid off due to AI could need to be reemployed at a higher rate
• Context, judgment and meaning are irreplaceable human traits that AI can't bring
• Companies need to reinvest in AI – not just accept mediocre gains

Gartner has argued that the future of work is moving beyond simple AI automation – instead, it says that using AI to enhance human capability and redesigning how work gets done are far more critical for future business leaders.

Fundamental to Gartner's report is the importance of human workers and the preservation of human judgment and context, therefore the analysts warn that AI-induced layoffs could be costly in the long-term despite offering potential savings today.

In fact, the study warns that by as soon as 2029, one in three (30%) employees laid off because their jobs were replaced by AI will need to be rehired, and potentially at a higher cost to their future employers than had they just been kept on.

Gartner warns about AI layoffs and the importance of human workers

Ultimately, the analysts warn that treating AI primarily as a headcount-reduction mechanism could weaken future talent pipelines, remove institutional knowledge and expertise, reduce innovation ability, and create expensive recruitment challenges later.

First of the four upcoming trends predicted by the company is the human-AI relationship – companies are being urged to build stronger human-AI collaboration where AI and automation are largely seen as augmentation, not replacement.

It's also about a cultural change, because Gartner says workforces should adapt to AI, not just adopt it. Employees will need to learn continuously, adapt to changing roles and understand how to work effectively alongside AI.

Thirdly, human context, judgment and meaning should be protected, with Gartner increasingly worried that AI could inadvertently strip these away.

Finally, Gartner expects AI investments to be a continuous loop and not a single action – companies that reinvest will likely be the ones to see the best success.

"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," VP Analyst Tori Paulman wrote.

If you really know your stuff about streaming services, you'll know that Apple TV is the home of consistently high-quality content — and its new Australian thriller Last Seen is no exception.

Based on Ryan David Jahn's novel The Dispatcher, we follow emergency dispatcher — and secret alcoholic — Ian Ridley (Patrick Brammall), who receives a call about a body found near his home. He thinks it may be his daughter, who went missing 11 years ago.

For those who have already read the book, there are a few big changes for its TV adaptation: most notably, that it's now set Down Under, not in Texas. Even so, a seemingly never-ending web of twists and turns is still set to unfold.

With the first two episodes of Last Seen now streaming, the remaining four episodes will drop on a weekly basis until October 7. And, according to Brammall, we're really not prepared for what's coming.

'Nothing is black and white'

"Ian is not a hero," Brammall exclusively told me about his character in the Apple TV Original. "His focus on paper is an altruistic one, but it's also mixed with hope, guilt, [and] everybody he's failed. There's a lot of collateral damage.

"Conversely, when we first see Sarah (Chloe Jean Lourdes), she's happy. Nothing's black and white. As the series goes on, you can sort of see that Ian and Henry (Brendan Cowell) are two sides of the same coin."

By the time episode 2 ends, which is where we're currently up to, this doesn't exactly feel like the case. Henry feels like an elusive villain, having clearly held onto some deeply disturbing secrets for too long, and for reasons we don't quite yet understand.

"By the end, the agency is taken away from them both and given to Sarah," Brammall continued. "I think that's completely unexpected. The ending is something that you wouldn't necessarily have anticipated, and it's not the sort of ending you'd usually get in a crime drama, either. That's a testament to how multi-layered the show is."

Intriguing. In this day and age, we should probably consider it a luxury when a crime drama is able to surprise us. If Last Seen can really put its money where its mouth over the next few weeks, then we've genuinely got some unmissable TV on our hands.

For much of the AI boom, the infrastructure conversation has centered on compute: chips, servers and the increasingly large data centers needed to support them. But as AI moves from experimentation to deployment at scale, technology leaders face another infrastructure challenge that could be just as consequential: securing enough reliable power, quickly, to keep that compute running.

AI data centers are fundamentally different from traditional commercial and industrial power customers. They are larger, more concentrated and exceptionally time-sensitive, with near-zero tolerance for interruption. That makes energy availability more than an operating consideration. Increasingly, it can determine where AI infrastructure gets built, how quickly it comes online and whether organizations can turn enormous technology investments into business value.

Natural gas is emerging as a bridge fuel for AI's power challenge

For technology leaders, the key question isn't simply whether enough electricity can ultimately be generated. It's whether firm, dispatchable power can reach a data center when and where it's needed.

That's where natural gas is playing an increasingly important role. Given constraints on other non-intermittent power alternatives, gas can provide the around-the-clock generation needed to support large AI workloads. PwC's scenario analysis shows the potential scale: even in our most conservative scenario, AI-linked gas demand reaches 5.2 billion cubic feet per day (Bcf/d) by 2030, compared with roughly 1.6 Bcf/d today. By 2035, our scenarios put demand between 7.6 and 11.5 Bcf/d.

For data center developers and technology companies, however, those numbers tell only part of the story. Having enough gas in the system doesn't mean it can necessarily reach a data center on the required timeline. It must be produced, transported, stored and delivered at the right pressure through connected infrastructure. In other words, AI's power challenge is increasingly becoming a deliverability challenge.

The scarce resource may be time, not capital

Technology companies are committing tens of billions of dollars to AI infrastructure, but money can't quickly solve many of the constraints standing between a planned data center and an operational one.

Permitting, pipeline rights-of-way, grid interconnections, turbines, water and skilled labor can all extend development timelines. Developers are simultaneously competing for critical equipment and industrial capacity while navigating local zoning and water constraints. That changes the calculus around infrastructure.

In a market defined by speed to deployment, an existing pipeline, permitted corridor, storage asset or available generation capacity can be more valuable than a theoretically lower-cost alternative that takes years to develop. For technology leaders making decisions about AI capacity, site selection therefore needs to account for much more than land, connectivity and eventual power availability. The ability to secure reliable energy on the required timeline should be considered much earlier in the process.

Energy procurement is becoming a strategic capability

We're already seeing data center developers respond differently. Behind-the-meter generation, for example, can allow a campus to pair on-site or nearby gas generation with firm fuel supply rather than relying solely on the traditional grid interconnection process. PwC estimates that more than 30% of AI-related gas demand could be behind the meter by 2035.

Other models are emerging as well, including dedicated pipeline laterals paired with generation and more integrated arrangements connecting gas supply, transportation, storage, generation and data center load. The larger lesson for business and technology leaders isn't that every data center should pursue the same energy strategy.

It's that energy procurement can no longer be treated as a back-office function that happens after the technology and real estate decisions have been made. It is becoming a strategic capability.

Building AI infrastructure will require a broader ecosystem

This shift also changes who technology companies need around the table. The next generation of AI infrastructure will require greater coordination among hyperscalers and data center developers with utilities, natural gas providers and pipeline operators. Increasingly, these parties will need to solve for the entire path from energy supply to operational compute rather than solely optimizing their individual piece of the equation.

For technology executives, that makes partnership strategy increasingly important. Securing energy infrastructure earlier, understanding regional constraints and developing relationships across the power ecosystem can help reduce schedule risk before billions of dollars of compute are waiting for power.

AI may be a technology revolution, but scaling it is quickly becoming a physical infrastructure challenge. The organizations best positioned for the next phase will be those that treat energy as a strategic capability and recognize that natural gas can play a critical role in providing the reliable, dispatchable power needed to bring AI capacity online and keep it running.

In the race to scale AI, time to power may ultimately determine time to value.

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• Apple’s announced new Pro controls and Photographic Styles for iPhone
• They’re limited to the new iPhone Duo and iPhone 18 Pro and series
• But Apple is extending its free satellite coverage for older iPhones

I take a lot of photos on my iPhone, so watching Apple’s iPhone Duo event yesterday, I was naturally intrigued by the news of fresh Photographic Styles and the introduction of Pro controls, both of which give you more power over your smartphone images. But it now looks like there’s a serious catch that wasn’t apparent at the time of the event.

In the hours since newly instated Apple CEO John Ternus wound down his keynote speech, iPhone fans have taken to social media to point out that both Pro controls and the third-generation Photographic Styles are limited to the current batch of iPhones.

Specifically, you’ll only be able to access them if you have an iPhone 18 Pro, iPhone 18 Pro Max or an iPhone Duo. Users of last year’s iPhone 17 series — or anything released before that — will be shut out of the new features.

And that’s a shame, because they look like they could be a real boon for photography enthusiasts. Pro controls include useful tools to adjust white balance, manually tweak aperture and shutter speeds, consult histograms, and more. Photographic Styles, meanwhile, are handy presets and filters you can apply to your images and now come with precise controls for grain and texture. But if you don’t have one of the latest iPhones, they’ll be out of reach.

Extended satellite coverage

(Image credit: Apple)

Despite this disappointing development, it wasn’t all bad news for owners of older iPhones. After the event, it emerged that Apple has quietly extended its free satellite connectivity features for another year, and that applies all the way back to the iPhone 14.

Specifically, you’ll get another year of satellite coverage if you have any device from the iPhone 14, iPhone 15 or iPhone 16 ranges, despite the fact that this deal was meant to have expired for these devices. Apple originally launched its satellite features for the iPhone 14 in 2022 and said that product would get two years of coverage, but has instead repeatedly extended its free terms for the iPhone 14 as well as the iPhone 15 and iPhone 16.

This extension applies to features including Emergency SOS via satellite, Messages via satellite, Find My via satellite, and Roadside Assistance via satellite. There’s no news regarding how much Apple might charge if it ever decides to end its free coverage, but now that’s not something you’ll have to worry about for at least another year.

With these features remaining free for the time being, owners of older iPhones have been granted a little ray of tech sunshine. Apple might be gatekeeping new Photographic Styles and Pro controls behind its latest devices, but the arguably more important satellite tools still won’t require a pricey upgrade.

• Evidence points to Beats 360 coming later in September
• That's despite Beats' owner, Apple, having just hosted a big launch event
• We now know a lot about these new headphones too…

While the AirPods 5 upgrades offer you the best features of the AirPods Pro, they were small consolation at an Apple Event in which people were hoping for more sweeping audio upgrades. No AirPods with cameras, no HomePods upgrade despite teases, and no new Beats headphones.

However, that latter point could be addressed later in the month. How come? because according to MacRumors, Apple just released a firmware update for the Beats 360 headphones. Yes, that same pair of Beats 360 headphones we've been hearing rumors about for months, but that have yet to be officially announced.

Why is Apple updating headphones that don't exist? Well, bear in mind we've been seeing them for months, as Apple gave them to multiple soccer World Cup players to show off during the tournament. Celebrity sportspeople need their tech updating too, after all.

There's no indication of what this update does, and it doesn't really matter. It's all about what this update suggests.

A Beats 360 release date just around the corner?

Because of this recent software update, many now think that the Beats 360 are due to be unveiled at some point in September.

While Apple already has just had its big product unveiling event of the year (on September 9, around 14 hours ago at the time of writing), that doesn't rule out new Beats devices. Apple generally lets its Beats sub-brand operate independently.

In fact, it would've been pretty bizarre if we had seen the Beats 360 shown off at the Apple event, for this fact.

We know a lot about how the Beats 360 look, thanks to plenty of leaks (and, of course, the fact that they were literally on the necks, heads and bags of a few soccer players months ago). They sound like a big step up from what we've seen, but as the launch of a new range, it's hard to know how they'll stack up to existing models.

Beats' last product launch was in October 2025, when it showed off the Powerbeats Fit, so maybe we'll see the brand take the same late-September-early-October window for its new device. Or devices? We've only heard about the Beats 360, but that doesn't rule out siblings of other form factors.

@techradar

♬ Candy - Lane 8 & Quentin Quivera

Reports keep saying Apple is releasing a home smart display. It still hasn’t happened, and here’s why that’s a problem.

• A new report from KB Securities gets very gloomy about the RAM crisis
• We're told the "memory chip market is heading toward a severe supply shortage" in 2027
• AI demand has ramped up massively with infrastructure investments up 60% year-on-year to $1.3 trillion, creating even more demand for memory as fuel for that rise

If you thought the RAM crisis was going to get worse next year, you're very probably right — but apparently we might suffer an even more severe shortage than some analysts already expect.

Wccftech flagged up an article from Business Korea (translated from Korean), which suggests that the RAM squeeze is tightening its grip on two of the biggest memory chip makers.

Citing a report from KB Securities, the rumor is that Samsung and SK Hynix are now down to fewer than 10 days of memory inventory, which is very close to running dry.

This has caused concerns that available memory supplies could dwindle and be depleted in 2027, with analysts believing that the "memory chip market is heading toward a severe supply shortage" (again, bear in mind this is a translation, but the message is clear enough).

KB Securities notes that the AI boom is what's causing this, as memory chip manufacturers struggle to meet demand for HBM4 memory in AI data centers. Infrastructure investments for AI development have been revised upwards to $1.3 trillion for 2027, we're told, which is a 60% year-on-year increase.

Kim Dong-won, head of research at KB Securities, told Business Korea that: "AI servers will absorb demand for not only HBM but also server DDR5 and enterprise SSDs, potentially leading to a historic shortage."

There's a brighter spot of news from laptop maker Framework though, which, as VideoCardz reports, has taken an unusual step in reducing the price of some of its products. As you may recall, Framework has been hiking laptop prices due to the increased cost of RAM (as have other notebook manufacturers), but the company just announced that it has secured some fresh memory modules at a lower price.

This means that some of its current Framework Laptop orders will be knocked down in price, and the company said on X that it's "retroactively reducing prices for orders that shipped at higher price" too.

Analysis: there's a historic level of gloom, that much is certain

(Image credit: vlado85rs / Getty)

It's impressive to see Framework acting in the best interests of its customers, particularly seeing as the firm could have (theoretically) kept quiet about bagging a batch of cheaper memory chips, and just kept the difference itself.

While that's commendable, it's a pretty small (but still welcome) bone we're being thrown in the face of the bad news for the RAM crisis here. Talk of a "historic shortage" is worrying, and it feels like this is an escalation of the recent chatter about global memory supply woes which has already turned highly negative.

While this latest report talks about server DDR5 RAM (and HBM), this will obviously have a bearing on consumer products, too. Those less profitable memory sticks are going to end up sidelined even further.

With rumors about GPU price hikes again coming to the fore this week, it's difficult to catch a breath between the bouts of bad news. We can hope that the pessimism is overblown here, but I can't shake the feeling that this seems unlikely.

So no, neither of these fold, but the iPhone 18 Pro and iPhone 18 Pro Max still feel like modern-day flagship smartphones after my quick hands-on right after Apple’s Surprise and Shine event.

While neither flagship device carries the mystique and intrigue of the iPhone Duo, both march forward with a laundry list of features and enhancements that will push the iPhone into the future for many.

As someone who’s spent a lot of time reviewing the entry-level iPhones, like the iPhone 17 and iPhone 16, which normally get the fun new colors, I was instantly struck by the new shades. Immediately, Burgundy for the iPhone 18 Pro and iPhone 18 Pro Max stood out — it’s a lovely dark shade of red that can shimmer a bit depending on how the light hits it, and it's definitely less of a pop than Cosmic Orange, but it's really just lovely.

Back in black

There’s also Glacier, a light blue that can come across as bluish silver depending on how the light hits it — and it was the color of the invite for Apple’s Special Event. Black, in a very matte, dark fashion, is back as a Pro color option alongside silver.

Aside from that, these new phones look and feel very much like the iPhone 17 Pro and iPhone 17 Pro Max in your hand — not a bad thing, as that ushered in a slightly sturdier build with the larger camera plateau.

Arguably the biggest hardware change lives here, though, and it’s variable aperture — which you can actually see in action, opening and closing, with your own eyes — for the 48-megapixel main Fusion camera. This should give the 18 Pro and 18 Pro Max a leg up in low light, as you can adjust — either manually with Pro Controls or by letting the iPhone set it automatically — aperture, shutter speed, and white balance.

You can pick between a set of four — ƒ/1.48, ƒ/1.8, ƒ/2.8, and ƒ/4.0 — apertures for the main lens and, depending on your pick, you can let more light in or less, so it handles multiple depths for subjects in one shot more effectively.

The new variable aperture lens. (Image credit: Jacob Krol/Future)

I played around with these and the new Pro Controls interface at the top of the camera app, and here’s the good news: it’s fully customizable, so you can place what is most helpful there.

I didn’t have much time with it, but it’s clear it does open and close the aperture window on the back of the phone, so it moves as Apple describes. You have way more controls now over your photos so you can get closer to what you’re really after. It's also coupled with enhancements to Photographic Styles that will let you add in texture or grain, as well as AI-infused photo editing features as part of iOS 27.

The new Pro Controls on the iPhone 18 Pro. (Image credit: Jacob Krol/Future)

This 48-megapixel main camera with variable aperture is paired with a 48-megapixel telephoto lens and a 48-megapixel ultra-wide lens. This means that the 18 Pro and 18 Pro Max can still shoot from 0.5x to 8x optical, and up to 40x digitally. So while the iPhone Duo folds, the 18 Pro and 18 Pro Max do still pack the Pro-grade camera system.

On the front, there’s still the impressive CenterStage Camera and Face ID stack with all the nifty features that the 17 Pro and 17 Pro Max ushered in, but the change is a dramatically smaller Dynamic Island. And it’s not a case of shrinking it for shrinking’s sake either — the increased screen real estate around the smaller island that’s centered at the top lets it show up to three Live Activities at once, and each of these can show a bit more detail.

For instance, if you have music playing at the top and a timer next to it, you previously couldn’t actually see how much time is left — that’s fixed now. I tried a mix of these — tossing Flighty, Maps, Music, Timer, and other apps at the top — and I do believe this will be genuinely useful. I’m not sure it’s an immediate reason to upgrade from, say, a 17 Pro, but those who swear by the Dynamic Island won’t be disappointed.

(Image credit: Jacob Krol/Future)

Those are the most evident changes I noticed from a brief hands-on, and it’s a toss-up for me between the Burgundy and Glacier shades and the camera upgrades for my favorite new feature. iOS 27 ran really smoothly on the unit I used in the hands-on space Apple provided, and that’s likely thanks to the two-nanometer A20 Pro chip under the hood. It’s Apple’s fastest yet and features ‘Super Cores’ in the CPU, much like Apple’s M-Class silicon for the Mac lineup.

It’s also paired with a new thermal architecture that includes a larger vapor chamber, which should keep the iPhone 18 Pro and 18 Pro Max a bit cooler, especially around the raised camera plateau on the back. Perhaps the biggest change I didn’t get to test is the longer battery-life promise. The 17 Pro and 17 Pro Max were no slouches in this department by any means, but Apple’s running laps year over year, with the 18 Pro now lasting as long as last year's 17 Pro Max and the 18 Pro Max going even further.

There’s a $100 price increase for these new devices in the United States — the iPhone 18 Pro starts at $1,199 for 256GB and the 18 Pro Max at $1,299 for 256GB. That’s a $100 increase generation over generation, and you can pay more to configure these up to 2TB. Both go up for preorder on September 12, 2026, and launch on September 18, 2026. There will be both carrier deals and the Apple Upgrade Financing route.

I’ll need to spend more time with both to see how much those changes actually matter in everyday use, but after this first hands-on, the iPhone 18 Pro and 18 Pro Max feel like a continuation of what Apple started with the 17 Pro generation rather than a complete rethink.

And honestly, that’s not a bad thing. The hardware still feels premium in hand, the new colors are excellent, the smaller Dynamic Island looks genuinely useful, and the variable aperture is the one change that immediately made me want to spend more time with the camera.

The iPhone Duo may have the flashier story this year, but the 18 Pro and 18 Pro Max still feel very much like the flagship iPhones to beat – and I’m especially curious to see what that camera can actually do once I get them out of Apple's hands-on area and into the real world.

@techradar

♬ Block Beat - Leo Lighthouse Junior

Hands-on: At Apple’s Sept. 9 event, I tried the new iPhone 18 Pro and Pro Max. The standout feature is the new variable aperture on the main camera.

• Verdant argues some figures are overestimated by as much as 4-5x
• Steel and vehicle manufacturing are more likely to support jobs
• Megawatt growth doesn't correlate with job growth

A new Verdant report has argued data centers are unlikely to create as many jobs as many companies and reports believe – its latest estimations put all currently planned UK data centers at 10,400 permanent operational jobs, compared with the 40,200 jobs that industry body techUK projects.

techUK also claims that current data centers that are already in operation already support 24,300 jobs – a figure that Verdant contests as being closer to 4,400.

Ultimately, Verdant criticizes other estimations including techUK's for using older figures based on smaller campuses, but as data centers grow and become increasingly automated, they require fewer staff.

Are data centers actually creating that many jobs?

In short, Verdant explains that increasing automation means compute capacity can expand without employment rising proportionally, so adding another megawatt of compute doesn't necessarily mean adding a fixed number of new workers. Therefore, its rough estimation of 1.2 jobs per MW for new developments isn't hugely representative of all facilities.

The report also expects employment density to decrease further as facilities get larger, comparing data centers unfavorably to steel plants, vehicle manufacturing plants, hospitals and schools.

"The briefing concludes that while data centres may offer other benefits, employment cannot credibly be the primary justification for continued rapid build-out," the company wrote, calling for a pause on construction and wider public discussion.

All of this, of course, against the backdrop of hyperscalers investing billions to grow their capacities, with each new campus and expansion promising hundreds or thousands of operational jobs and tens of thousands of related roles across the likes of construction.

The iPhone Air 2 was a no-show at Apple Park on Wednesday.

Researchers say that they exploited the microphone and content recognition features of an LG TV to listen to user conversations.

Some of the most interesting features won’t ship with the iPhone Duo, iPhone 18 Pro, Apple Watch Series 12 and Ultra 4.

The iPhone 18 Pro and Pro Max and the buzzy foldable iPhone Duo bring some great stuff. But there are some downsides, too.

The iPhone 18 Pro, Pro Max and iPhone Duo are coming soon, but the baseline model is presumably coming next year, likely for these reasons.