The first foldable iPhone has finally arrived. Following its announcement at Apple's launch event, select attendees were given time to try the device, and the early impression is that the iPhone Duo feels genuinely different from the slab phones that have defined the last decade. The long-running question was whether Apple would enter the foldable market only when the technology was good enough. The iPhone Duo is the company's answer, and the hands-on experience suggests that the wait was used to refine the hinge, the display, and the moment-to-moment feel of folding and unfolding.

Key Facts at a Glance

• Product: iPhone Duo, Apple's first foldable iPhone.
• Display: 7.6-inch internal screen with a matte finish and a 5.4-inch outer screen.
• Design: Book-style fold with a 1.4:1 aspect ratio, described as squatter and closer to a passport book when closed.
• Durability: IP68 rating for water and dust resistance.
• Hinge: Smooth folding mechanism with a crease that is almost impossible to find by touch.
• Software behavior: Gradual transition when opening and closing, with content appearing to come into focus.
• Modes: Laptop mode with Netflix controls on the bottom half and tent mode with StandBy active without a charger.
• Cameras: Dual main camera setup with 48-megapixel photos and 2x optical zoom.
• Camera comparison: Largely similar to the iPhone 18 Pro but without the variable aperture.
• Availability: Announced at Apple's Surprise and shine event.

A Design That Feels Different in the Hand

The iPhone Duo immediately stands out because of its inner screen. The display has a decidedly matte finish, which handled glare from windows above the hands-on area better than many glossy foldable panels. That matte quality does not appear to sacrifice clarity, and it gives the device a more paper-like feel when reading or scrolling through long pages. The folding mechanism is smooth, and Apple has managed to make the crease almost impossible to find with a finger. That is a meaningful achievement because the crease has been one of the most common complaints about foldable phones since the category began.

The outer screen measures 5.4 inches, which looks petite by modern slab phone standards. But that is part of the point. When the iPhone Duo is closed, it is meant to be a compact phone that can be used one-handed. When it is open, the 7.6-inch internal display becomes the main event. The 1.4:1 aspect ratio is squatter than some early rumors suggested. It is not simply two iPhone Airs placed side by side. Instead, it resembles a passport book when closed, which makes the internal screen wider and better suited for watching videos in landscape. In portrait, that extra width is also useful for reading long scrolling pages or running two apps side by side in multitasking.

The Open and Close Experience

The real show happens when the phone is opened or closed. Rather than simply switching one screen off and lighting the other up, the iPhone Duo uses a gradual transition. The contents of the screen appear to come into focus as the device moves toward its final position. It is a small touch, but it makes the foldable feel more deliberate and polished. The transition also helps the two displays feel like parts of one continuous experience rather than two separate panels competing for attention.

In laptop mode, the iPhone Duo was shown with Netflix on the inner screen. The bottom half of the display carried playback controls before fading them away. That setup turns the foldable into a small, self-supporting viewing device. It is not a replacement for a tablet or laptop, but it suggests that Apple is thinking about how the hinge can create new interaction models rather than just a larger canvas. Tent mode offers another example. StandBy was active even without the phone plugged in, which means the always-on style information can appear while the device rests in a tent shape. That raises an obvious question about battery life. Running a large internal display, an always-on mode, and a foldable hinge system could place significant demands on the battery, and real-world testing will be needed to see how the iPhone Duo holds up.

Why Apple Waited

Rumors of a foldable iPhone have been circulating since at least 2017. Samsung released its first foldable two years after that, and since then almost every major Android manufacturer has entered the market, including Google, Motorola, Huawei, Xiaomi, and Oppo. Each of those companies helped push foldable hardware forward, but they also revealed the category's growing pains. Early devices struggled with display durability, hinge reliability, creases, software scaling, and battery life. Apple's approach has typically been to enter product categories later with a more polished design, and the iPhone Duo appears to follow that pattern.

Over the last decade, Apple was reported to be developing several different concepts. Some reports described traditional book-style folding iPhones. Others pointed to smaller flip phone variants. There were even suggestions of larger foldable iPads. The company eventually settled on the form factor now known as the iPhone Duo. Early rumors suggested it would be like having two iPhone Airs side by side, but the final design uses a 1.4:1 aspect ratio. That makes the closed device squatter and the open device wider. The result is a foldable that prioritizes landscape video and portrait reading, a combination that could distinguish it from other foldables on the market.

Camera System and Performance Details

The iPhone Duo features a dual main camera setup that shoots 48-megapixel photos and includes 2x optical zoom. The system is largely the same as the iPhone 18 Pro, but it lacks the variable aperture found on that model. For everyday photography, the 48-megapixel sensor should provide plenty of detail, and the 2x optical zoom offers a useful middle ground between the main lens and a longer telephoto. The absence of variable aperture may matter most to advanced photographers who want finer control over depth of field and light intake. Still, the camera hardware appears competitive with other flagship phones, and the foldable form factor adds new possibilities for framing, propping the phone, and using the outer screen as a viewfinder.

The IP68 rating is another important detail. Foldables have historically been more vulnerable to dust and water because of their moving parts. An IP68 rating means the iPhone Duo should survive accidental splashes and dust exposure, though it is not a license to take it swimming. That level of protection is a sign that Apple has confidence in the hinge and display sealing. It also puts the iPhone Duo in line with modern flagship expectations rather than treating foldable durability as an exception.

Software and Multitasking Potential

The 7.6-inch internal screen opens up new possibilities for iOS. Multitasking on a display that wide could make split-screen apps more usable than they are on a standard phone. Reading long articles, editing photos, or watching a video while chatting are all scenarios where the extra width helps. The 1.4:1 aspect ratio is not as wide as a full tablet, but it is wider than many book-style foldables, which should make landscape video look more natural. The outer 5.4-inch screen, meanwhile, keeps the device usable when closed. That dual-display approach means users can choose between a compact phone experience and a larger canvas without carrying two devices.

The gradual transition animation when opening and closing is more than a visual flourish. It suggests that Apple is treating the foldable as a single device with two states, not as a phone with a tablet attached. If third-party apps take advantage of the transition and the larger inner display, the iPhone Duo could offer a more cohesive experience than earlier foldables. Developers will need guidance on how to adapt layouts, handle state changes, and make the most of the outer screen. The launch event included design guidance for app developers, which indicates that Apple wants the ecosystem ready for the new form factor.

What to Watch For

The iPhone Duo arrives in a market that is more crowded than it was when foldable rumors first began. Samsung, Google, Motorola, Huawei, Xiaomi, and Oppo all have foldable experience, and each has learned lessons about what works and what does not. Apple's late entry means the iPhone Duo will be compared against mature competitors. Its advantages appear to be the matte inner display, the smooth hinge, the minimal crease, and the polished open-close transition. Its challenges include battery life, app optimization, price, and durability over years of use.

The 5.4-inch outer screen may divide opinion. Some users will appreciate its compact size, while others may find it too small for typing, browsing, or watching video. The 1.4:1 aspect ratio also changes the shape of the inner display compared with other foldables. It is wider for landscape video, but it may create letterboxing in some apps or require developers to rethink layouts. Those are the kinds of trade-offs that only real-world use will reveal.

The camera system is another area to watch. A dual 48-megapixel setup with 2x optical zoom is strong on paper, but the lack of variable aperture means it is not identical to the iPhone 18 Pro. Photography enthusiasts will want to compare low-light performance, portrait mode, and zoom quality. The foldable design could also introduce new camera features, such as using the outer screen as a preview for selfies or setting the phone in tent mode for hands-free shots. Apple has not detailed every software feature yet, but the hardware foundation appears capable.

Battery life remains the biggest unknown. The iPhone Duo has a large inner display, a secondary outer display, a complex hinge, and always-on features like StandBy in tent mode. All of that draws power. If Apple has managed to optimize the chipset and display technology, the phone could last through a full day. If not, users may find themselves reaching for a charger more often than they would with a traditional iPhone. That question will not be answered until full reviews and extended testing.

Source: The Verge News

Apple announced the iPhone 18 Pro and iPhone 18 Pro Max at its September event, positioning the pair as the most capable camera phones the company has ever shipped. The new Pro models arrive in deep black, silver, glacier, and burgundy, with color-matched back glass. They introduce the A20 Pro chip, a new C2 cellular modem, and a variable-aperture main camera that Apple calls the biggest camera improvement in years. The iPhone 18 Pro starts at $1,199 for 256GB, a $100 increase over the iPhone 17 Pro, while the iPhone 18 Pro Max starts at $1,299 for 256GB. Both models will be available in 256GB, 512GB, 1TB, and 2TB configurations. Preorders begin September 12, ahead of availability on September 18.

Design and Display Changes

The iPhone 18 Pro line refines the redesign introduced with the iPhone 17 Pro and Pro Max in 2025. That earlier generation dropped titanium in favor of an aluminum body, added a vapor chamber to manage heat, and introduced a camera bar that stretches across the back. The new phones continue that design language but add a smaller Dynamic Island that can display three Live Activities at once. Apple also updated accessories, including a revised clear case. The color palette includes deep black, silver, glacier, and burgundy, with color-matched back glass that gives the phones a more unified appearance. The smaller Dynamic Island is a subtle but practical change for users who rely on Live Activities for sports scores, ride updates, food delivery, and timers.

A20 Pro Chip and Performance

At the heart of the iPhone 18 Pro is the A20 Pro, built on a 2nm process. Apple says the chip uses a brand new architecture, similar to the approach taken with its M6 chip for Macs. The A20 Pro includes a 6-core CPU with two desktop-class super cores and four efficiency cores. Its 7-core GPU is described as significantly advanced, delivering a 40 percent increase in performance. Apple has also added a second neural engine, bringing the total to 32 NPU cores. That extra AI horsepower is designed to support Apple Intelligence and Siri AI, including more expressive voices. For users, the combination should mean faster on-device processing for photography, real-time language tasks, and generative features without relying as heavily on the cloud.

Battery Life and Charging

Battery life gets a major boost. Apple says the iPhone 18 Pro offers all-day battery life comparable to the iPhone 17 Pro Max. Video playback is rated at 36 hours for the iPhone 18 Pro, while the iPhone 18 Pro Max reaches 45 hours. The phones use a next-generation vapor chamber with three times the surface area of the one in the 17 Pro, which helps keep temperatures down during gaming, video recording, and AI tasks. Charging is also faster: 15 minutes of wired charging provides a 50 percent charge for the iPhone 18 Pro. For the iPhone 18 Pro Max, five minutes of wired charging delivers enough power for seven hours of video playback. Those improvements address a common complaint about heavy phone use, especially as cameras and AI features demand more power.

Variable Aperture Camera

The headline feature is the 48 megapixel variable-aperture main camera. It uses a six-blade aperture with blades thinner than a human hair, along with a new sensor. In darker environments, the aperture opens to let in more light, which Apple says makes night mode captures faster and better in quality. The variable aperture gives photographers more control over depth of field and exposure, similar to what a traditional camera lens can do. On a phone, this is a significant engineering challenge because the camera module must remain thin while moving parts reliably. Previous phones have experimented with variable aperture, but Apple's implementation combines the hardware with computational photography and the A20 Pro's neural engine. The result should be more natural background blur, better low-light shots, and greater flexibility for portrait and landscape photography.

Video and Pro Controls

Video capabilities also expand. The iPhone 18 Pro can record 4K resolution time-lapse videos in Dolby Vision HDR. Apple is adding Pro controls to the Photos app, giving users more manual control over camera settings. Users can change shutter speed to tweak motion blur, for example, and Pro controls are available for both photos and videos. Photographic styles receive improvements, including texture and grain control. Apple will also support Google's SynthID content authenticity protocol, as well as a new Apple Reference Image standard that cryptographically signs images at capture, creating an unalterable digital negative. These features matter as AI-generated imagery becomes more common; they help verify that a photo came from a real camera and has not been altered. The combination of manual controls and authenticity tools positions the iPhone 18 Pro as a tool for professionals and serious enthusiasts, not just casual snapshots.

C2 Modem and Connectivity

The iPhone 18 Pro line is the first set of phones with Apple's new C2 cellular modem. The C2 supports mmWave 5G and delivers meaningfully faster uploads compared to the C1X while consuming 15 percent less energy. The modem is part of Apple's broader effort to control more of its silicon stack, reducing dependence on external suppliers. Faster uploads benefit video calls, cloud backups, live streaming, and social media posting. Lower energy consumption helps battery life, especially in areas with weak signal where modems often work harder. The C2 modem also supports the latest 5G bands, making the phones more future-proof as carriers expand their networks.

Apple Intelligence and Siri AI

Apple highlighted how the new phones are well-equipped to work with Apple Intelligence and Siri AI. The second neural engine and 32 NPU cores are designed to handle on-device machine learning tasks. Siri AI gains more expressive voices, making interactions feel more natural. Apple Intelligence features include writing tools, notification summaries, image generation, and contextual responses. The A20 Pro's architecture and the increased neural capacity should allow these features to run faster and more privately on the device. For users, that means less waiting and more useful assistance in daily tasks. The combination of hardware and software also supports real-time translation, advanced photo editing, and personalized suggestions without sending as much data to the cloud.

Pricing and Market Context

The $1,199 starting price for the iPhone 18 Pro represents a $100 increase over the iPhone 17 Pro. The iPhone 18 Pro Max starts at $1,299. Both come with 256GB of base storage, and configurations go up to 2TB. The price hike reflects broader trends in the smartphone market, including higher component costs, advanced chip manufacturing, and inflation. Apple is positioning the Pro line as a premium product for professionals, creators, and enthusiasts who want the best camera and performance. Competitors have also raised prices, and the overall market has seen a shift toward more expensive flagship devices. The 2TB option is notable because it supports high-resolution video, large photo libraries, and offline AI models. Preorders begin September 12, and the phones go on sale September 18.

What It Means for Photographers

For photographers, the variable aperture is the most interesting change. A physical aperture allows the camera to control how much light reaches the sensor and how much of the scene is in focus. On most phones, aperture is fixed, and depth of field is simulated in software. A variable aperture can produce more realistic background blur and better exposure in bright light, where a fixed aperture might force a faster shutter speed or lower ISO. The six-blade design suggests Apple has engineered a durable mechanism that can fit inside a thin phone. Combined with the new sensor and Pro controls, the iPhone 18 Pro could appeal to users who want more manual control without carrying a dedicated camera. The support for SynthID and Apple Reference Image also addresses growing concerns about authenticity in photography.

Battery, Cooling, and Long-Term Use

The larger vapor chamber and efficient A20 Pro chip should help the iPhone 18 Pro sustain performance over longer sessions. Gaming, 4K recording, and AI processing generate heat, which can cause throttling and battery drain. By increasing the vapor chamber surface area, Apple aims to keep the phone cooler for longer. The battery ratings of 36 and 45 hours for video playback are impressive, though real-world results will vary based on screen brightness, network conditions, and app usage. Fast charging improvements reduce downtime: a 15-minute charge to 50 percent on the iPhone 18 Pro is convenient for users who need a quick top-up. The Pro Max's five-minute charge for seven hours of video playback is a notable claim that could change how people think about charging

Source: The Verge News

Apple is preparing for what could be one of its most consequential product launches in years. The company's 'Surprise and shine' event is set to begin at 10AM PT / 1PM ET, and the stakes are unusually high. This is not just another September iPhone refresh. It is the first iPhone keynote led by Apple's newly appointed CEO, John Ternus, who previously served as the company's vice president of hardware engineering. Ternus has already described the launch as 'a huge launch . . . that's going to be phenomenal.' That language suggests Apple sees this event as a turning point, not a routine upgrade cycle.

The headline attraction is expected to be the long-rumored foldable iPhone. Multiple signs and rumors point to Apple finally entering the foldable market with a device that could be called the iPhone Duo. If the rumors are correct, it would be Apple's first foldable phone and a new halo product for the company. A foldable iPhone would also give Ternus a dramatic way to step into the spotlight as CEO, putting his hardware background front and center.

But the event may be unconventional in more ways than one. Apple is also expected to discuss operating system updates, new Apple Watches, and possibly new AirPods. At the same time, the standard iPhone 18 may not arrive until the new year, leaving the more expensive Pro models and the foldable phone to carry the initial launch. And with what industry watchers have dubbed 'RAMageddon' driving up component costs, there is a chance that everything announced will be more expensive than in previous years.

Key facts at a glance

• The event is Apple's 'Surprise and shine' keynote, scheduled for 10AM PT / 1PM ET.
• It is the first iPhone keynote led by new Apple CEO John Ternus, formerly VP of hardware engineering.
• A foldable iPhone is widely expected, potentially named iPhone Duo.
• Apple is likely to announce new Apple Watches, AirPods, and OS updates.
• The standard iPhone 18 may be delayed until the new year.
• iPhone 18 Pro and Pro Max are expected, with a dynamic aperture main camera and the return of a black Pro color.
• RAMageddon and rising memory prices could push prices higher across the lineup.
• Hands-on impressions and staff reactions are already part of the broader event coverage.

The foldable iPhone Duo takes center stage

For years, Apple has watched competitors experiment with foldable phones. Samsung, Google, Huawei, and others have released devices that bend, flip, and unfold, but Apple has remained on the sidelines. That patience may finally be ending. The rumored iPhone Duo is expected to be Apple's first foldable, and it could reshape how developers think about iPhone apps.

A foldable iPhone would not simply be a phone that opens into a tablet. It would require a new approach to multitasking, app continuity, and user interface design. Apple has reportedly prepared design guidance for app developers, helping them adapt their software to a device that can shift between multiple screen sizes and postures. That guidance could cover everything from split-screen layouts to how apps behave when the device is partially folded.

The name 'Duo' is also notable. It suggests a device built around two states or two halves, and it fits Apple's tendency to use simple, descriptive branding. If the iPhone Duo is real, it will likely be positioned as a premium product, sitting above the iPhone 18 Pro and Pro Max in both price and prestige. That would make it a true halo device, much like the original iPhone or the first Apple Watch.

iPhone 18 Pro and Pro Max: Dynamic aperture and a black finish

Even with the foldable phone dominating attention, Apple's traditional Pro line is not expected to be ignored. The iPhone 18 Pro is rumored to feature a dynamic aperture main camera, a feature that would give photographers more control over depth of field and exposure. A dynamic aperture could allow the camera to adjust physically, rather than relying solely on software, which would be a significant step for mobile photography.

Another detail that has surfaced is the return of the black iPhone Pro. Color options may seem minor, but they can have an outsized effect on buyer behavior. A black Pro model has been a popular choice in the past, and its return would give the Pro lineup a more understated, professional look. Early hands-on impressions of the iPhone 18 Pro and Pro Max are already part of the conversation, suggesting that the devices will be available for demonstrations shortly after the keynote.

Apple Watch, AirPods, and OS updates

Apple's September events have traditionally been about more than just the iPhone. New Apple Watches are likely, possibly with updated health sensors, faster chips, and new case finishes. Apple has been steadily expanding the Watch's health features, and any new model would be expected to continue that trend.

AirPods may also appear, though the rumors are less specific. One notable point is that there are no AirPods with cameras yet, and at least some observers hope it stays that way. The idea of adding cameras to earbuds raises privacy and practicality questions, and Apple has not signaled that such a product is imminent.

On the software side, Apple is expected to outline the next major versions of its operating systems. That includes updates for iPhone, iPad, Mac, Apple Watch, and Apple TV. These updates often set the stage for new hardware features, and a foldable iPhone would require deep software integration. Developers and users will be watching closely to see how Apple adapts its interfaces for a device that can change shape.

RAMageddon, pricing, and a changing market

One of the biggest uncertainties hanging over the event is price. The memory market has been under severe pressure, a situation sometimes called RAMageddon. Rising costs for RAM and other components could force Apple to raise prices across its product line. That would come at a time when smartphone prices are already trending upward across the industry.

If the standard iPhone 18 is delayed until the new year, Apple may focus its September event on higher-end models. That strategy would allow the company to prioritize premium devices with better margins, even as input costs rise. It would also make the foldable iPhone Duo an even more important product, since it could justify a higher price point and attract early adopters.

The competitive landscape is another factor. Foldable phones have matured over several generations, and rival devices now offer polished hinges, improved displays, and better durability. Apple is entering later than many competitors, but it has a history of entering categories after others and then redefining them. The iPod, iPhone, iPad, and Apple Watch all followed that pattern. Whether the iPhone Duo can do the same will depend on execution, software, and developer support.

What to watch during the keynote

The event is scheduled to begin at 10AM PT / 1PM ET. Viewers can expect a tightly produced presentation, likely with a mix of product videos, onstage demos, and technical explanations. The most important moments will be the first look at the foldable iPhone Duo, the camera details on the iPhone 18 Pro, and any pricing announcements.

Also worth watching is how John Ternus approaches his first iPhone keynote as CEO. His background is in hardware engineering, and he has been involved in some of Apple's most important product transitions. A successful event could establish him as a leader who can both manage Apple's existing product lines and push the company into new form factors. A cautious or overly iterative event, by contrast, could raise questions about whether Apple has lost its appetite for risk.

Developers will be listening for details about app design guidance for the iPhone Duo. If Apple provides clear tools and frameworks, third-party apps could adapt quickly. If the guidance is vague, the foldable experience could feel uneven at launch. That is a familiar challenge for any new form factor.

Finally, the broader context matters. Apple is holding this event amid rising component costs, a shifting regulatory environment, and increasing competition in AI-powered features. The company is expected to weave AI and machine learning into its new devices, even if the keynote focuses on hardware. The combination of a new CEO, a new foldable, and a tougher pricing environment makes this one of Apple's biggest events in years.

Source: The Verge News

After the presentation, attendees were able to hold and try the devices. The immediate takeaway is that the iPhone 18 Pro and Pro Max retain much of the design language of the previous generation. The aluminum body, overall proportions, and screen sizes carry over, giving the phones a continuity that will feel instantly recognizable to anyone who has used a recent Pro model.

Familiar design with new color-matched glass

The iPhone 18 Pro lineup is available in four colors: black, silver, glacier, and burgundy. The glacier finish is a shade of blue, while burgundy adds a deeper, warmer option to the mix. One of the more subtle design changes is the use of color-matched glass on the back. This glass blends more seamlessly with the aluminum frame, reducing the visual contrast that has sometimes separated the camera plateau and rear panel from the rest of the phone.

That may sound like a small detail, but in a market where premium phones are often differentiated by finish and feel, it matters. The color-matched glass gives the iPhone 18 Pro a more cohesive look, especially in the burgundy and glacier finishes where the rear panel and frame can appear more unified. The phones still feel solid and dense, with the same general weight and balance as their predecessors.

A smaller Dynamic Island with more Live Activities

The Dynamic Island is smaller on the iPhone 18 Pro and Pro Max. Apple says the reduced size is possible because of changes to the front-facing sensor array and display integration. More importantly for everyday use, the smaller island can now display three Live Activities at once. In a hands-on setting, this works as described. Users can keep track of multiple ongoing tasks, such as a timer, a sports score, and a ride-share status, without the island expanding into an overly large black pill.

The Dynamic Island has evolved since its debut as a way to turn the camera cutout into a functional interface element. With the iPhone 18 Pro, it becomes a denser information hub. The ability to see three Live Activities simultaneously could be particularly useful for travelers, sports fans, and anyone who relies on real-time updates while moving between apps.

Variable-aperture main camera

The biggest new feature is the variable-aperture lens on the main camera. This is a first for an iPhone, and it gives photographers and videographers more control over depth of field and exposure. The aperture can be set to adjust automatically, or users can select one of four stops: f/1.48, f/1.8, f/2.8, and f/4.0. Each stop changes how much light reaches the sensor and how much of the scene remains in focus.

A wider aperture like f/1.48 lets in more light and can produce a shallower depth of field, which is useful for portraits and low-light shots. A narrower aperture like f/4.0 lets in less light but increases the depth of field, helping more of the scene stay sharp. The automatic mode is designed to choose the best setting based on the scene, but the manual stops give advanced users the ability to make deliberate creative choices.

Pro controls arrive in the native camera app

Apple has also added pro controls to the native camera app. This is a significant change for users who have relied on third-party camera apps to access manual settings. The new controls include adjustable shutter speed, white balance, and focus. The focus control includes peaking in the preview, which highlights the edges of objects that are in focus. This is a nice touch for photographers who need to confirm critical focus quickly, especially when shooting with a shallow depth of field.

The inclusion of manual controls in the default camera app reduces the friction of switching between apps. It also signals that Apple is listening to pro users who want more direct control over image capture without leaving the standard iOS camera experience. The variable aperture and pro controls together make the iPhone 18 Pro feel more like a serious camera that also happens to be a phone.

What the changes mean for everyday photography

For most users, the automatic mode will handle the new aperture system without any intervention. The camera will decide when to open up for low light and when to stop down for landscapes or group shots. But for enthusiasts, the manual stops and pro controls open up new creative possibilities. A portrait photographer can choose a wider aperture to isolate a subject. A landscape photographer can choose a narrower aperture to keep foreground and background sharp. A videographer can adjust shutter speed to control motion blur.

The focus peaking feature is particularly useful for manual focus shooting. It provides a visual confirmation that the intended subject is sharp, which can be difficult to judge on a small screen. White balance control allows users to match the color of the light in a scene or to deliberately warm or cool an image for creative effect. Shutter speed control can be used to freeze action or to introduce motion blur for a sense of movement.

Pricing and availability

The iPhone 18 Pro and iPhone 18 Pro Max will be available to preorder starting September 12th ahead of a September 18th release date. The iPhone 18 Pro starts at $1,199, while the iPhone 18 Pro Max starts at $1,299. These prices place the phones at the premium end of the smartphone market, continuing a broader trend of rising flagship prices.

The release timing follows Apple's usual pattern of announcing new iPhones in September and shipping them shortly after. Preorders will open on September 12th, giving customers a chance to secure a device before the September 18th launch. The four color options and two screen sizes mean buyers can choose between a more compact Pro model and a larger Pro Max model with the same core features.

How the iPhone 18 Pro fits into Apple's Pro lineage

Apple's Pro line has gradually become the testing ground for the company's most advanced camera and display technologies. Over the years, the Pro models have introduced features like ProMotion displays, multiple rear cameras, LiDAR scanners, macro photography, ProRAW, and higher-resolution sensors. The iPhone 18 Pro continues that tradition by bringing a variable aperture to the main camera and adding pro-level controls to the native camera app.

The variable aperture is a notable addition because it is a mechanical change, not just a software feature. It allows the lens to physically adjust the size of its opening, which affects both light gathering and depth of field. This is a capability that has long been available in dedicated cameras, and its arrival on an iPhone marks a step forward for mobile photography.

The pro controls in the camera app also reflect a shift in how Apple approaches professional workflows. In the past, users who wanted manual control often turned to third-party apps. By building these controls into the native app, Apple makes them more accessible and more integrated with the rest of the system. This could encourage more users to experiment with manual settings and could also make the iPhone 18 Pro more appealing to professional photographers and videographers.

What else was announced at the event

The 'Surprise and shine' event was not limited to the iPhone 18 Pro and Pro Max. Apple also used the presentation to showcase other products and technologies, including a foldable iPhone Duo and other hardware updates. The event generated a wide range of announcements, from design guidance for app developers to new accessories and services. For the Pro phones, however, the main story is the combination of a refined design, a smaller Dynamic Island, and a more capable camera system.

The foldable iPhone Duo, in particular, drew attention as Apple's first entry into the foldable category. While the iPhone 18 Pro and Pro Max remain traditional slab-style phones, the presence of a foldable model in the lineup suggests that Apple is exploring multiple form factors for the future. That could eventually influence how the Pro line evolves, but for now the iPhone 18 Pro stays true to the design language that has defined recent generations.

First impressions and early analysis

From a design standpoint, the iPhone 18 Pro and Pro Max are iterative rather than radical. The aluminum body and screen sizes are unchanged, and the overall shape will be familiar. The new colors and color-matched glass add a fresh look, but the phones do not represent a dramatic break from the past. That is not necessarily a bad thing. Apple has refined a formula that works, and the changes it has made are focused on areas that matter to photographers and power users.

The smaller Dynamic Island is a welcome improvement. It reduces the amount of screen area occupied by the camera cutout and makes the island feel less intrusive. The ability to display three Live Activities at once makes it more useful as a glanceable information center. These are the kinds of changes that improve the day-to-day experience without requiring users to learn a new interface.

The variable aperture and pro controls are the headline features. They give the iPhone 18 Pro a level of manual control that was previously unavailable in the native camera app. The four aperture stops offer a useful range, from a very bright f/1.48 to a more restrained f/4.0. The automatic mode ensures that casual users can still get good results without thinking about settings. The manual controls are there for those who want them.

Pricing will be a key consideration. At $1,199 and $1,299, the iPhone 18 Pro and Pro Max are expensive. The higher price reflects the advanced camera system, the new display technology, and the overall premium positioning of the Pro line. Whether the new features justify the cost will depend on how much users value manual camera control and the smaller Dynamic Island.

Preorders begin on September 12th, and the phones will be released on September 18th. The iPhone 18 Pro starts at $1,199, while the iPhone 18 Pro Max starts at $1,299. The lineup comes in black, silver, glacier, and burgundy, with color-matched glass on the back. The Dynamic Island is smaller and supports three Live Activities at once. The main camera has a variable aperture with four selectable stops, and the native camera app includes pro controls for shutter speed, white balance, and focus. Focus peaking is available in the preview to help confirm what is in focus.

Source: The Verge News

Peso Pluma has secured 17 nominations for the 2026 Latin Music Awards, making him the most nominated artist of the year. The Mexican superstar's haul includes a coveted Artist of the Year nod, a Top Latin Album of the Year nomination for Dinastia with Tito Double P, and a Regional Mexican Artist of the Year, Solo mention. The awards recognize achievement across 47 categories in Latin pop, tropical, Latin rhythm, and regional Mexican music.

Close behind Peso Pluma is Tito Double P with 15 nominations, followed by Fuerza Regida with 12. Karol G earned nine nods, Prince Royce received eight, and Bad Bunny scored seven. The nominations reflect the continued global rise of regional Mexican music and the enduring popularity of Latin pop, tropical, and rhythm stars.

Top Nominees at a Glance

• Peso Pluma: 17 nominations
• Tito Double P: 15 nominations
• Fuerza Regida: 12 nominations
• Karol G: 9 nominations
• Prince Royce: 8 nominations
• Bad Bunny: 7 nominations

Peso Pluma's Dominant Year

Peso Pluma, born Hassan Emilio Kabande Laija in Zapopan, Jalisco, has become one of the most influential figures in contemporary Latin music. His blend of corridos tumbados, trap, and regional Mexican sounds has earned him a massive international following. The 17 nominations for the 2026 Latin Music Awards underscore his impact on the genre and his ability to cross over into mainstream markets. His album Dinastia, a collaborative project with Tito Double P, has been a commercial and critical success, earning a Top Latin Album of the Year nomination. The project highlights the chemistry between the two artists and their shared vision for modern Mexican music.

In addition to Artist of the Year and Top Latin Album of the Year, Peso Pluma is competing for Regional Mexican Artist of the Year, Solo. That category places him against other solo acts who have shaped the genre over the past year. His nominations span performance, album, and genre-specific fields, demonstrating his versatility and consistent output. Over the past few years, Peso Pluma has released a string of hits that have topped charts and generated billions of streams. His live performances have also drawn huge crowds, solidifying his reputation as a global touring force. The 2026 awards could further cement his status as a leading voice in Latin music.

Tito Double P and Fuerza Regida Close Behind

Tito Double P follows Peso Pluma with 15 nominations. The Mexican singer and rapper has become a key collaborator and a standout artist in his own right. His work with Peso Pluma on Dinastia is a major factor in his nomination count, but he has also earned recognition for his solo material. Tito Double P's rise reflects the collaborative spirit of regional Mexican music, where partnerships and guest

Source: Music-News.com News

The Television Academy has unveiled a deep and eclectic roster of presenters for the 78th annual Emmy Awards, set to take place Monday at the Peacock Theater in downtown Los Angeles. The list blends current television favorites with film stars, comedy legends, and familiar faces from across decades of pop culture. Among those confirmed to appear onstage are Zendaya, David Harbour, and Florence Pugh, three performers whose careers have moved fluidly between prestige television and major motion pictures.

Zendaya, already an Emmy winner for her lead performance in Euphoria, remains one of the most closely watched actors of her generation. David Harbour, known for his role as Jim Hopper in Stranger Things, has become a fixture of streaming-era television. Florence Pugh, an Oscar nominee for Little Women, has also made an impression in Marvel projects and independent films. Their participation signals that the Emmys are still able to draw A-list talent even as the television landscape fragments across an ever-growing number of platforms.

The Academy also named Sarah Michelle Gellar, Amy Poehler, and Jon Hamm as presenters. Gellar, forever associated with Buffy the Vampire Slayer, represents a bridge between genre television and mainstream stardom. Poehler, a former Saturday Night Live cast member and the star of Parks and Recreation, is one of the most recognizable comedic voices in television. Hamm, who won an Emmy for Mad Men, remains a perennial awards-show favorite. Together, they bring a mix of nostalgia, comedy, and prestige drama to the ceremony.

Other presenters set to appear include Odessa A’zion, David Boreanaz, Linda Cardellini, Macaulay Culkin, Alan Cumming, Sally Field, Marcello Hernández, Kate Jackson, and Peter Krause. The roster continues with Cheryl Ladd, Dan Levy, Julianna Margulies, Annie Murphy, Niecy Nash-Betts, Keke Palmer, Amanda Peet, Rachel Sennott, and Jaclyn Smith. That grouping spans several generations of television history, from the era of Charlie’s Angels and Scarecrow and Mrs. King to modern hits like Schitt’s Creek, Russian Doll, and The Bear.

A Host Making History

Mariska Hargitay, the longtime star of Law & Order: Special Victims Unit, will host the ceremony. Her selection is notable not only because of her decades-long association with one of television’s most durable dramas but also because she is the first woman to host the Emmys in 15 years. Hargitay has played Captain Olivia Benson since the late 1990s, earning critical acclaim and a devoted fan base. In recent years, she has also taken on more producing responsibilities, helping to shape the series that made her a household name.

In a recent interview, Hargitay said the ceremony is about celebrating the extraordinary work that people in television are fortunate enough to do: telling stories that move audiences, entertain them, and make them laugh and cry. She added that humor and laughter are deep and indispensable parts of who she is, and that she feels lucky to bring all of herself to the event. That tone of warmth and gratitude is likely to shape the evening, even as the competition among nominees remains fierce.

The telecast will be executive produced by Jesse Collins, Dionne Harmon, and Jeannae Rouzan-Clay of Jesse Collins Entertainment, alongside Hargitay. It will air on NBC and stream on Peacock starting at 8 p.m. Eastern and 5 p.m. Pacific. The Peacock Theater, located in the heart of downtown Los Angeles, has become a regular home for major awards shows, offering a sleek, modern setting for a night that celebrates both commercial success and artistic achievement.

Nomination Leaders and the Shape of the Race

Heading into Monday, HBO Max’s The Pitt leads all nominees with 25 nods. The drama has become a critical and popular sensation, and its strong showing suggests that voters are eager to reward ambitious, character-driven storytelling. Close behind is HBO Max’s Hacks with 24 nominations. The comedy has been a consistent awards favorite, blending sharp industry satire with a complicated, affectionate central relationship. Its tally confirms that the Academy continues to value long-running series that can evolve without losing their voice.

Apple TV+’s Widow’s Bay follows with 19 nominations, while Apple TV+’s Pluribus earned 18. Netflix’s Beef collected 16 nominations, and HBO Max’s DTF St. Louis received 13. NBC’s Saturday Night Live and Amazon Prime Video’s Spider-Noir each landed 11 nominations. The spread of nominees across broadcast, cable, and streaming platforms reflects a television industry that no longer has a single center of gravity. Legacy networks, premium cable channels, and global streamers all compete for the same prizes, often with radically different business models and release strategies.

The dominance of streaming services in the nomination count is not surprising. Over the past decade, the Emmys have increasingly recognized series that premiere on platforms rather than traditional networks. Yet the presence of Saturday Night Live, a broadcast institution, and Law & Order: Special Victims Unit, a long-running network drama, shows that established brands still matter. The Academy’s membership appears to reward both innovation and endurance.

Why Presenters Matter

Awards shows are often judged by their hosts, but the presenter lineup is just as important. Presenters set the tone, provide star power, and help bridge the gap between niche critical favorites and broad popular appeal. By mixing film actors like Zendaya, Florence Pugh, and Sally Field with television stalwarts like David Boreanaz, Kate Jackson, and Jaclyn Smith, the Emmys are signaling that television is not a closed ecosystem. It is a medium that attracts talent from every corner of the entertainment industry.

The inclusion of younger performers such as Odessa A’zion, Marcello Hernández, and Rachel Sennott points to an effort to reach viewers who may not have grown up watching traditional broadcast TV. At the same time, legends like Alan Cumming, Cheryl Ladd, and Annie Murphy appeal to different demographics. Dan Levy, Julianna Margulies, Niecy Nash-Betts, Keke Palmer, and Amanda Peet add further depth, representing comedy, drama, and genre work across multiple eras.

Additional presenters are expected to be announced in the coming days. That rolling announcement strategy is common for major awards shows, as producers look to build momentum and keep audiences guessing. The final list could include even more surprise appearances, but the current roster already suggests a ceremony designed to feel both celebratory and broadly representative of the industry.

The Road to Monday

The 78th Emmy Awards arrive at a moment of transition for television. Production schedules have been reshaped by labor disputes, streaming economics, and changing viewer habits. The shows that succeeded this year did so in a crowded marketplace, competing for attention against an endless supply of new content. That context makes the nominations for series like The Pitt, Hacks, Widow’s Bay, Pluribus, Beef, DTF St. Louis, Saturday Night Live, and Spider-Noir especially meaningful. Each represents a different approach to storytelling, from serialized drama to sketch comedy to limited-series experimentation.

For the presenters, the Emmys offer a chance to celebrate their peers and to remind audiences of the breadth of their own careers. Zendaya, for example, has moved from Disney Channel stardom to Emmy-winning dramatic work. David Harbour has balanced blockbuster franchise roles with character-driven television. Florence Pugh has built a reputation for intense, emotionally raw performances in both independent cinema and major studio films. Their presence onstage will likely generate some of the night’s most memorable moments.

Hargitay’s hosting duties add another layer of significance. As a longtime advocate for survivors of sexual assault and domestic violence, she has used her platform to support real-world causes. Her public profile extends beyond acting, and her Emmy hosting role will bring additional attention to the issues she has championed. Whether she leans into comedy, nostalgia, or heartfelt reflection, her opening monologue will be one of the evening’s most anticipated segments.

The ceremony will also serve as a reminder of the Peacock Theater’s role as a gathering place for the entertainment industry. Located in downtown Los Angeles, the venue has hosted concerts, award shows, and special events, becoming a familiar backdrop for televised celebrations. For the Emmy Awards, it provides a stage large enough for elaborate production numbers and intimate enough for the spontaneous moments that often go viral.

As the final preparations get underway, the presenter list stands as a statement of intent. The Emmys are not just about who wins. They are about the community of creators, performers, and craftspeople who make television possible. The 78th annual ceremony will honor that community with a lineup that spans generations, genres, and platforms. From Zendaya and David Harbour to Florence Pugh and Mariska Hargitay, the night promises a mix of glamour, humor, and recognition for the stories that defined the past year in television.

Hargitay, reflecting on the opportunity, said she feels very lucky to bring all of herself to the party. That sentiment may well capture the spirit of the evening: a celebration of television as a shared cultural experience, and an acknowledgment that even in a fragmented media landscape, the medium still has the power to bring people together.

Source: The Hollywood Reporter News

Curl is one of the most important open-source programs in the world. It is embedded in Linux distributions, operating systems, cars, routers, cloud infrastructure, and countless software applications. The tool handles a huge share of internet data transfers, so any vulnerability in it demands serious attention. Finding a genuine unknown defect in Curl is not like solving a contrived benchmark; it means discovering a mistake that survived years of real-world auditing, fuzzing, and production use. That is why the Curl maintainers’ confirmation of Aisle’s findings carries more weight than a typical AI vendor demo.

Key facts everyone should know

• Aisle reported 29 candidate security issues during its recent analysis of Curl.
• Curl maintainers accepted six of those findings as newly identified vulnerabilities and assigned CVE numbers.
• The six CVEs were patched in Curl 8.22.0, released on September 2.
• Curl’s own advisory database lists the six CVEs as low severity.
• Daniel Stenberg, Curl’s founder, confirmed that other AI tools such as Anthropic’s Mythos, OpenAI Codex Security, and ZeroPath did not surface additional vulnerabilities at the time.
• Greg Kroah-Hartman, stable Linux kernel maintainer, said he is seeing similarly interesting results for Linux.

These facts should not be oversold. Six low-severity CVEs from one project do not prove that Aisle is universally better than every other AI security product. But they do provide real-world evidence that a purpose-built vulnerability-management system can find issues that general-purpose AI models miss when asked to review mature production code.

How Aisle finds vulnerabilities

Aisle does not claim to be built on the world’s most powerful AI model. Instead, the company argues that a well-orchestrated system can outperform a frontier model when the task is finding security bugs across a large codebase. The core idea is that even small models can recognize a vulnerability if they are given the right snippet of code with enough leading context. The challenge is deciding which snippets matter, where to look, and how to validate what the model thinks it has found.

Aisle’s platform reportedly combines agent orchestration, codebase-wide analysis, vulnerability hypotheses, validation loops, and reproduction workflows. It also works to triage candidate findings before presenting them to humans. That means maintainers receive fewer noisy reports and more reports that have already survived a round of automated validation. This kind of systematic approach matters because one of the biggest failures of early AI security scanners was not a lack of intelligence but a lack of focus. They generated enormous numbers of false positives, duplicates, and irrelevant findings, which consumed maintainer time without producing merges.

The startup’s broader pitch is that AI should not stop at discovery. Aisle says it can also generate patch suggestions and verification steps that help developers turn a confirmed bug into a merged fix more quickly. If that workflow holds up in practice, it would reduce one of the biggest bottlenecks in open-source security: not finding the bug, but having a qualified maintainer review, patch, test, and release it.

The maintainers’ perspective

Daniel Stenberg, who created Curl and remains the project’s lead maintainer, was initially skeptical of the flood of AI-generated security reports. Over time, though, he described the current moment as a high-quality chaos era. He expressed gratitude that powerful AI engines are scanning Curl, because finding flaws means they can be fixed. He also noted that Aisle seems to invest real engineering effort in producing curated results. The project’s maintainers are more likely to take every Aisle report seriously because the reports arrive with useful context and fewer false alarms.

Greg Kroah-Hartman, the Linux stable kernel maintainer often called Greg KH, shared a similar reaction on Mastodon. He wrote that he is seeing the same kind of results for Linux and said: I have no idea what Aisle is doing differently, but wow. That comment is significant because Kroah-Hartman deals with an enormous volume of security fixes and has little patience for AI-generated noise. His approval suggests Aisle is providing something beyond generic machine-scanning output.

Jim Fuller, a Red Hat senior principal software engineer focused on security, has speculated that Aisle understands the limitations of its tooling and has worked harder than simply running a scanner. Maintainers have also worked directly with Aisle, meeting, talking, and refining the output. This communication appears to be an important part of the process. A responsible AI security tool should not only find bugs; it should also respect the realities of how open-source maintainers review reports, issue CVEs, and write patches.

A valid result, with important limits

Aisle originally reported 29 candidate issues in Curl, but only six cleared the project’s security review bar and became CVEs. That does not make the other 23 reports worthless. Some may have been ordinary bugs rather than security vulnerabilities, some may have been duplicates, and some may still be under review. But the gap between 29 findings and 6 confirmed CVEs is a reminder that findings and confirmed vulnerabilities are not the same thing.

The fact that the accepted CVEs are low severity is another reason to avoid overstatement. Low severity means the vulnerabilities likely require unusual conditions, limited attacker control, or other mitigating factors. Still, low-severity bugs in a tool as widely deployed as Curl are worth fixing. They can become part of a chain when combined with other weaknesses, and removing them reduces the attack surface.

This outcome also highlights the value of validation by the project’s own maintainers. In most AI security comparisons, the vendor running the test controls whether a finding is counted. Here, Curl’s maintainers made the decisive call. They reviewed Aisle’s evidence, decided which reports represented security vulnerabilities, assigned CVE identifiers, created fixes, and shipped those fixes in a public version of the project. That process is far more credible than a score on a synthetic benchmark or a capture-the-flag exercise.

System versus model

Aisle is using the Curl results to advance what it calls a system-over-model argument. In that view, the quality of an AI security product depends less on the raw intelligence of the underlying model and more on the orchestration around it. A general-purpose model may be excellent at reasoning about code in the abstract, but it can produce inconsistent results when it is asked to scan an entire mature project in one pass. A specialized system may gain an advantage by continuously iterating over code paths, tracking configuration-specific behavior, correlating historical vulnerability patterns, and testing candidate issues before it presents them to a human.

This argument is reasonable. Many AI coding tools are built around a chat interface where a developer asks a question about a small piece of code. That works well for explaining functions or generating examples, but it is not the same as systematically hunting for subtle security flaws across a codebase with thousands or millions of lines of code. A tool designed for vulnerability hunting can incorporate threat models, security rules, and repository history into its scans. It can also keep track of what it has already examined and what it still needs to explore.

The front-end model matters, of course. No amount of orchestration can find a vulnerability if the underlying model cannot recognize unsafe code patterns. But the Curl result is an argument that model capability is not the only constraint. The surrounding system can compensate for some model limitations by giving the model the right context and by checking its conclusions through additional steps.

Why maintainer approval matters

Security tool comparisons have long struggled with the difference between a laboratory success and a production finding. Benchmark datasets contain known flaws, which means AI models can be overfitted to them, either deliberately or accidentally. Synthetic tasks may not reflect the messiness of real code. Vendor-verified results can be biased by the vendor’s own product goals. In the Curl case, none of those problems were central. The code was not chosen by Aisle, the vulnerabilities were not already known, and the evaluators were independent maintainers with a reputation for caution.

Curl’s codebase is also a difficult test. It is written in C, a language with many memory-safety pitfalls, but it has been reviewed by experts for decades. Any surviving bug is likely to be subtle and dependent on unusual usage patterns. The fact that Aisle found six such bugs suggests that its approach can add value even in an environment where traditional code review has already been thorough.

What this means for AI security tools

The Curl episode does not settle the broader question of which AI security tool is best. One successful result must be repeated across other projects, programming languages, and software architectures before it becomes a general pattern. Enterprises and open-source maintainers should still ask hard questions about how a tool handles false positives, how it validates its findings, and how much human review is required before a patch is ready.

But the episode does provide a clear lesson: real-world maintainer verification is the most meaningful signal available when evaluating an AI security product. A tool that can find correct, previously unknown vulnerabilities in a mature open-source project, and present them well enough for overworked maintainers to accept them, deserves attention. That is why the praise from Stenberg, Kroah-Hartman, and others in the open-source security community matters. They have seen countless AI-generated reports, and they are not easily impressed.

For security teams that have become skeptical of AI coding assistants, Aisle’s Curl results are a useful counterexample. The tool still needs to prove that it can perform consistently across many different codebases. But when some of the most respected maintainers in the world say they are seeing real vulnerabilities discovered by an AI system, it is worth investigating how that system works. If it can deliver high-quality, validated findings without burying maintainers in noise, Aisle may become an important part of the open-source security toolkit.

Source: ZDNET News

Only three dedicated detectors identified every sample correctly. Another popular service, which had earned a perfect score in an earlier round, collapsed to a 20 percent accuracy rate. Meanwhile, several general-purpose chatbots outperformed most of the tools built specifically for AI detection. The findings suggest that content detectors should be treated with caution, and that a chatbot might be as useful as almost any specialized product.

Why AI-generated plagiarism matters

The definition of plagiarism is central to this debate. Plagiarism means taking the words or ideas of another person and passing them off as one’s own without crediting the source. Using an AI writing tool such as ChatGPT does not involve theft in the traditional sense, but when the output is presented without attribution, it still fits that definition. Students, journalists, and marketers increasingly rely on AI-generated text, and that has created demand for tools that can flag it.

Yet detection tools are not neutral referees. They are statistical models that try to estimate the likelihood a text came from a large language model. Because they rely on probability, they make mistakes. The latest tests show that those mistakes are not marginal. Human-written columns were flagged as AI, some AI-generated pieces were judged human with high confidence, and one detector even refused to make a call.

How the test was constructed

The evaluation used five blocks of text. Two were drafted by the author; three were generated by ChatGPT. Each test block was submitted separately to every detection service, and the service response was compared with the known origin of the text. A result above 70 percent confidence was treated as the tool’s final answer, whether that answer indicated human or AI authorship. That made scoring simple: each correct identification earned one point, with a maximum of five points per detector.

The full group of standalone services included BrandWell, Copyleaks, GPT-2 Output Detector, GPTZero, Grammarly, Originality.ai, Pangram, QuillBot, Undetectable.ai, Writer.com, and ZeroGPT. One previously tested service, Monica, was removed because its free tier limited tests to 250 words and then required a costly upgrade. Pangram joined the test for the first time and immediately delivered a perfect result.

What the latest results show

Across 55 individual tests, accuracy ranged from 100 percent to only 20 percent. The three perfect performers were Pangram, QuillBot, and ZeroGPT. Pangram, built by former Google and Tesla engineers, also earned a perfect score in its very first appearance in the series. QuillBot, which had been wildly inconsistent in earlier tests, delivered a perfect score for the second consecutive round. ZeroGPT improved from an earlier score of 80 percent to a perfect result and has maintained that level in this latest run.

At the other end of the scale, Undetectable.ai scored 20 percent. It identified human-written text as 60 percent likely to be AI, then judged three ChatGPT-created samples to be 75 percent, 76 percent, and 77 percent likely human. Writer.com also struggled, assigning human origin to every sample despite three of the five having been written by ChatGPT. BrandWell and Grammarly each managed only 40 percent accuracy.

Several established names landed in the middle. Copyleaks scored 80 percent, but it misclassified the first human-written sample as 100 percent AI authored. GPTZero also scored 80 percent, yet it was unable to render a judgment on one human sample and identified another human sample as AI. Originality.ai earned 80 percent but made a striking error: it declared a human-written essay to be 100 percent AI generated.

Do chatbots work better

Because the test results were uneven, the evaluation also considered whether everyday AI chatbots could serve as AI detectors. The same five text blocks were submitted to ChatGPT free tier, ChatGPT Plus, Microsoft Copilot, Google Gemini, and Grok, with a simple prompt asking whether each block was written by a human or an AI.

ChatGPT Plus, Copilot, and Gemini all returned perfect scores. The free version of ChatGPT missed one human-written sample but correctly identified the other four. Grok, despite its reputation for strong reasoning, missed three of five samples and concluded that every block was human-written.

There was one especially surprising result. When the first human-written passage was submitted to the free version of ChatGPT in a private and unsigned session, the chatbot not only classified it as human text but also identified the author by name. The model drew on patterns in online articles that the author had written, demonstrating that context can sometimes help a model reach a conclusion that a specialized detector cannot.

Why accuracy is so volatile

AI detection scores change for many reasons. Models update, services add restrictions, and language models improve. In this round, some companies that had performed well in the summer declined after introducing limits on free usage. Others changed their scoring thresholds. The result is a moving target for schools, media organizations, and anyone who manages a website.

False positives are especially dangerous. A writer who chooses words carefully, or a non-native speaker who follows formal grammar rules, can be wrongly accused of using artificial intelligence. The tests suggest this risk is very real. One detector claimed human writing was fully AI-generated; another did the opposite and cleared all three AI samples as human.

What this means for editors and teachers

No single tool is safe enough to be the only line of defense. The original human samples sometimes looked too polished to a detector, while the ChatGPT samples were not polished enough to trip other detectors. Editors should treat a detection report as a starting point for discussion rather than as proof. Teachers should ask students to explain their work and preserve early drafts. Publishers should combine software checks with human review.

It is also worth remembering that the most common AI detectors are not academically certified instruments. Many are commercial marketing products. Their accuracy claims often overstate real-world performance, and independent checks continue to show that their scores can be influenced by content length, formatting, and the specific language model used to create the text.

Winners in a crowded market

Pangram stands out as a useful new option. Its five free scans per day fit the needs of occasional users, and it delivered correct verdicts for all samples. QuillBot, after a rocky start in earlier evaluations, now looks far more reliable. ZeroGPT has matured from a bare-bones website into a proper service while also improving its scoring. For people who need a second opinion on a piece of writing, these three are the strongest standalone candidates in the current field.

Among chatbots, Copilot, Gemini, and ChatGPT Plus all earned perfect results in this test. Since many people already pay for or have access to these assistants, they offer a no-extra-cost method of checking text for AI origins. The free version of ChatGPT is a reasonable fallback, but it is not perfect.

Because AI writing tools continue to evolve, these rankings are not permanent. Services that score perfectly today may degrade tomorrow after an update, just as several did during this testing cycle. New entrants, such as Pangram, can quickly rise to the top. The most reliable approach is to compare several tools, understand their limitations, and never rely on a probability number alone. As the tests continue, the most important lesson remains: both dedicated detectors and general chatbots can help, but they should be used as assistants to human judgment, not replacements for it.

Source: ZDNET News

CHARLOTTE, N.C. — September 3, 2026 — The Broadcast Retirement Network (BRN) today is marking seven (7) years of daily, advertising-free, fact- and evidence-based programming dedicated to retirement, aging, finance, lifestyle, privacy, and wellness. 

Since its launch in 2019, BRN has aired more than 2,500 original programs, broadcasting seven days a week at 7:30 AM ET. Over that period, the network has produced more than 625 hours of programming featuring international experts sharing their expertise with the audience. 

BRN programming is made available at no cost, complete with full transcripts, and is broadly syndicated across major news sites, aggregation services, streaming platforms and podcast services—ensuring BRN’s content reaches its audience wherever media is consumed. Shorter clips are also distributed across all major social media channels every two hours, further extending the network’s reach.

BRN distinguishes itself through a straightforward editorial commitment: no sales pitches, no advertisements, and no politics—just the facts, every morning. The network also delivers a daily, hand-curated newsletter The Morning Pulse on aging, finance, lifestyle, privacy, retirement, and wellness, selected by an expert editor.

“Reaching our seventh anniversary is a testament to the trust our audience and partners have placed in us,” said Jeffrey Snyder, Chief Executive Officer and Lead Anchor of the Broadcast Retirement Network. “For seven years—drawing on my 32 years in the retirement industry, our mission has remained the same: to deliver clear, credible and useful information to the people who need it, free of any external noise.

A September-Only Anniversary Offer

To mark the occasion, BRN is offering new subscribers 20% off The Morning Pulse—its daily newsletter delivering expert-curated news on money, health, and retirement, written by a human, without the use of AI, and free of ads and sales pitches. Every subscription directly supports BRN’s daily programming. Read today’s edition here: https://us6.campaign-archive.com/?u=26e6dd4c63255e9ef5e07a4c4&id=152e013f21

Normally $4 per month, new subscribers can save 20% with code BRN20 through September 30, 2026, only. Individuals can subscribe at https://buy.stripe.com/4gw9CS5NI8cibVm7su.

Looking Ahead

BRN will unveil a new opportunity for prospective partners on September 9, 2026. Details will be shared across the network’s platforms and social media channels.

“This milestone belongs to our guests, audience and partners as much as it does to us,” Snyder added. “We are grateful for seven years of continued support—and we’re just getting started.”

About Broadcast Retirement Network

The Broadcast Retirement Network (BRN) is an independent daily program delivering fact-based news and expert insight on aging, finance, lifestyle, privacy, retirement, and wellness. Airing seven days a week at 7:30 AM ET, BRN provides advertising-free programming, complete with transcripts, syndicated at no cost across major news, streaming, and podcast platforms. BRN is led by Chief Executive Officer and Lead Anchor Jeffrey Snyder, who brings 32 years of retirement industry experience to the network’s daily coverage.

Media Contact

Jeffrey Snyder 

Chief Executive Officer / Lead Anchor 

Email: jeff@broadcastretirementnetwork.com 

YouTube: https://www.youtube.com/@BroadcastRetirementNetwork

###

• Email: jeff@broadcastretirementnetwork.com
• Website: https://www.youtube.com/@BroadcastRetirementNetwork
• Company: Broadcast Retirement Network
• Company Logo:
• Name: JEFFREY H SNYDER
• Phone No: 6464840188
• City: Charlotte
• State: NC
• Country: United States

<p>The post Broadcast Retirement Network Marks 7 Years of Daily, Fact- and Evidence-Based Programming first appeared on PR Business News Wire.</p>

The statistic is a milestone because it underscores a fundamental shift in Google’s approach to product-related health content. Rather than treating video as a separate format that users click on only after reading standard results, AI Overviews are now treating YouTube as a primary source of explanatory knowledge. For the multi-billion-dollar supplement industry, this changes the rules for brand visibility, content marketing, and consumer education.

What the data reveals about AI Overviews

Analysts tracked a diverse set of popular search phrases related to supplements, including individual nutrients, dietary formulas, and wellness products. YouTube emerged as a source in more than half of the AI Overviews that were generated. This dominance was not limited to one category. It appeared across vitamins, minerals, herbal extracts, amino acids, and other supplement segments. The videos most frequently cited often combined personal experience with product reviews, ingredient breakdowns, and clinical-sounding explanations of how supplements might interact with the body.

The pattern suggests that Google’s algorithm perceives YouTube videos as especially useful for queries with commercial intent. Many supplement shoppers are uncertain about dosages, formats, or ingredient labels. A video that demonstrates a supplement being opened, tasted, and evaluated over several weeks can feel more trustworthy than a static article. Because YouTube videos include transcripts, Google can scan spoken content and connect the dots between the user’s query and specific claims inside the video. This makes video citations technically simple to include inside an AI-generated paragraph.

• YouTube appeared in 53 percent of AI Overviews for vitamin and supplement searches.
• The finding applies across multiple categories, from multivitamins to single-nutrient supplements.
• Query phrasing mattered: product comparisons and “best of” queries were particularly likely to include video citations.
• Official supplement brand websites were more visible in Overviews for precise brand-name searches, while YouTube creators were more visible for broad ingredient questions.

Why video has become a default answer format

There are several structural reasons behind YouTube’s strong presence in this niche. First, Google owns YouTube, and the two platforms have a deeply integrated relationship. The search engine can analyze video metadata, speech patterns, visual chapter markers, and engagement signals with far greater ease than it can extract meaning from other video-sharing sites. YouTube’s massive amount of health-related content also makes it a reliable inventory of answers. When someone searches for “benefits of zinc”, YouTube likely has thousands of videos from nutritionists, doctors, athletes, and everyday users who have shared their observations.

Another factor is the richness of video content. Supplements are products with tangible features: capsules, powders, gummies, dosage instructions, flavor notes, and packaging. Video allows creators to show these details, making it easier for a viewer to understand a product before purchasing. Many successful supplement YouTubers have built loyal audiences by posting daily or weekly updates about their health journeys, blood test results, and subjective reactions. This kind of narrative content is difficult to reproduce with conventional web copy, which is one reason why Google may be choosing video results for AI-generated answers.

The rise of YouTube citations also fits with the larger trend of consumers expecting multimedia answers. People often prefer to watch a short video while cooking or exercising instead of reading several paragraphs of text. Google has recognized this behavior and is adapting its AI Overviews accordingly. For health queries, where nuance matters, the presence of a human voice and face may appear to offer a valuable layer of explanation and reassurance.

The risks of blending YouTube with health guidance

The heavy use of YouTube in supplement-related Overviews is not without concerns. Dietary supplements are regulated differently depending on the country. In the United States, the Food and Drug Administration generally does not approve supplements for safety or effectiveness before they are sold. Instead, manufacturers are responsible for making sure their products are safe and that label claims are not misleading. That system opens the door to a range of claims that may not be supported by robust scientific evidence. When Google adds a YouTube video to an AI Overview, it can accidentally endorse questionable advice by presenting it in the same authoritative space as a government agency or a peer-reviewed study.

Another risk is the potential for harmful interactions. Many people use prescription medications and then search for a vitamin that might help with a symptom. A YouTube video that recommends a high dose of a supplement may be fine for some people but dangerous for others. AI Overviews often compress complicated information into a short paragraph, and the presence of a video thumbnail can make the answer feel more definitive than it actually is. Consumers may not realize that the cited YouTube creator is not a licensed clinician, or that the creator has not reviewed the user’s medical history.

There is also a financial dimension. YouTube creators often make money through affiliate links and sponsored partnerships. A glowing recommendation that appears in an AI Overview may be driven by a sponsorship arrangement rather than independent research. When the AI Overview does not explain that relationship, the line between advertisement and education becomes blurry.

What the new dynamic means for supplement brands

For brands in the supplement space, the growing role of YouTube in AI Overviews is both a challenge and an opportunity. Companies that have never invested in video marketing may be left out of AI-generated answers entirely. Search visibility is no longer just a question of publishing optimized product descriptions and blog posts. It now requires a coordinated video strategy that anticipates the questions consumers ask and provides content that a search engine will view as authoritative.

Brands should start by identifying the products and keywords that matter most to their revenue. For each target term, they need to understand whether an AI Overview is likely to appear and, if so, which sources it is pulling in. Then they can create videos that fill gaps in the current set of cited sources. A useful video might include a certified nutritionist explaining the science behind an ingredient, or a transparent production tour showing how a supplement is manufactured. The goal is to create content that provides enough detail and credibility to be considered as a source by Google’s AI model.

• Publish regular educational videos that address common supplement questions in plain language.
• Include clear timestamps and chapter markers so AI models can identify the most relevant portions of a clip.
• Add detailed transcripts and transcripts with structured data to help search algorithms understand the video’s content.
• Build authority by featuring qualified health professionals and citing real studies.
• Avoid making unsubstantiated medical claims; content that violates platform policies can also be excluded from AI Overviews.

At the same time, brands should not abandon text content. The most sophisticated AI Overviews often combine different types of sources. A page that includes both a high-quality article and an embedded explainer video may be more likely to be referenced than either format on its own. Marketers should see YouTube as a complement to their written content, not a replacement.

What consumers should do before clicking or purchasing

Because AI Overviews are generated by probabilistic models, they can include inaccuracies even when the source video is factually sound. The models summarize and rephrase content, which can introduce errors. For supplement questions, those errors can lead to incorrect dosing or an inappropriate supplement choice. Consumers should never treat an AI Overview as the final medical word. Instead, they should use the summary as a starting point, click through to the original source, and evaluate the expertise of the person delivering the advice.

Viewers should check whether the YouTube channel discloses sponsorships, has a transparent author bio, and distinguishes between personal opinion and scientific evidence. Credentials are not always necessary, but they are relevant. A video produced by a board-certified physician who specializes in nutrition should carry more weight than one made by an anonymous influencer with no medical background. Users should also compare multiple sources before buying anything, especially if the supplement promises dramatic results.

For people who are already taking medication or managing a chronic condition, it is wise to speak with a healthcare provider before adding a supplement. An AI Overview can offer a broad summary, but only a clinician can assess potential interactions, appropriate dosage, and whether the supplement is even necessary.

The evolving context of AI search and health information

Google’s move to incorporate YouTube into AI Overviews is part of a broader redesign of search. The company has said that AI tools can help people find information more quickly by distilling search results into concise answers. Yet that efficiency has raised important questions about trust, transparency, and the future of internet publishers. Early versions of AI Overviews were criticized for hallucinating facts or pulling from satirical sites. For health searches, the margin for error is especially thin.

YouTube has long been one of the most visited platforms in the world, and its library of health information is vast. The fact that it appears in 53 percent of supplement-related AI Overviews is not necessarily a negative outcome. Many videos contain high-quality explanations from qualified professionals. But when an AI system treats all YouTube content as equally reliable, the risk of misinformation increases. Supplement makers, medical experts, and platform developers must work toward a balanced approach that preserves the helpfulness of video while preventing harmful or misleading content from becoming part of AI-generated answers.

AI Overviews remain a moving target. The algorithms change frequently, and today’s citation patterns may shift as Google updates its models. What is likely to stay constant is the need for trustworthy, accessible health content. Web-based articles, videos, and AI summaries will continue to share space on the results page. For supplement brands, creators, researchers, and consumers, the lesson is to remain vigilant and to keep asking a simple question: is this information accurate enough to affect my health?

Source: AI News News

Why grid operators are paying attention

Operating an electricity grid has become significantly harder as coal and gas plants are replaced by solar panels and wind turbines. Unlike thermal power plants, which can be ramped up or down on demand, wind and solar generation depends on when the wind blows and when the sun shines. That makes accurate weather information a core business requirement rather than a useful extra.

Grid operators must continuously balance supply and demand. If a forecast is wrong on the low side for solar generation, they may have to purchase expensive reserve power or ask customers to reduce consumption. If a forecast is wrong on the high side, they may need to curtail renewable generation and waste cheap, clean electricity. With higher renewable penetration, these balancing decisions occur more frequently and with less warning.

Traditional numerical weather prediction models require enormous supercomputers and still take hours to run. They divide the atmosphere into three-dimensional grids and solve physical equations to simulate how weather systems evolve. Millions of calculations are completed before a forecast is delivered. While these models are reliable, they are expensive and not always fast enough to support trading and operations in fast-moving energy markets.

What WeatherNext 3 brings to the energy market

WeatherNext 3 is designed as an alternative for users who need high-resolution, regularly updated forecasts. Rather than solving the laws of physics from scratch, an AI weather model learns patterns from decades of historical atmospheric observations and reanalysis data. Once trained, it can generate a forecast in seconds or minutes, which makes it possible to update predictions more frequently and produce a larger set of possible outcomes.

This approach is particularly suited for the energy industry because market participants rarely need just one scenario. A utility trying to rebuild a storm-damaged network needs to know the likely path and strength of a hurricane. A wind farm operator bidding into a daily power auction needs to quantify the risk that wind speeds will be lower than expected. A solar trader wants to know how cloud cover will develop if changing atmospheric conditions are uncertain.

Probabilistic forecasts provide that kind of information. WeatherNext 3 produces probability distributions rather than single-value forecasts, allowing grid operators to make decisions that balance risk and cost. For example, it can show that there is a 60 percent chance that wind generation will exceed a certain threshold at a specific hour, alongside a 20 percent chance that it will fall below another threshold. Grid operators can then decide how much reserve capacity to keep available.

From research model to commercial product

Google has been developing AI weather models for several years. The modeling team focused on graph neural networks, which treat the atmosphere as a series of connected nodes and allow weather patterns to propagate across distances more efficiently than some traditional frameworks. WeatherNext 3 is part of that line of research, but it is aimed less at academic weather enthusiasts and more at enterprise customers.

The push into the energy market is logical. Energy is among the most forecast-dependent industries, and trading desks already spend large sums on weather consulting and data services. A machine learning model that can improve forecasts for wind, solar, temperature, and precipitation could save utilities millions of dollars through better scheduling, lower reserve costs, and reduced penalties in power markets.

Grid operators also need to anticipate extreme events. Cold snaps, heat waves, droughts, and storms are becoming more frequent and more intense. A forecast that underestimates peak electricity demand during a heat wave can trigger blackouts. An AI system that flags an unusual temperature pattern earlier may give operators time to call up emergency generators, secure additional power imports, or launch demand-response programs.

WeatherNext 3, like other modern AI models, uses a technique called autoregressive forecasting. After being trained on historical data, the model generates a prediction for one time step and then feeds that prediction back into itself to generate the next step. This process continues until it produces a forecast for the desired horizon. The output can include variables such as wind speed at different altitudes, temperature at the surface and upper levels, precipitation, and atmospheric pressure. Those variables are essential for calculating the output of wind turbines and solar panels.

A fast-growing competitive field

Google is not alone in this space. European weather centers have opened their data libraries to AI researchers, and several private companies are rolling out weather models based on foundation-model technology. These systems use self-supervised learning and massive parameter sets to take a more flexible approach to atmospheric modeling. Some are marketed to insurers, logistics providers, and agriculture companies as well as energy firms.

For Google, entering the energy market means competing on more than just forecast speed. Grid operators and trading firms will want the model to provide a long enough forecast horizon to support operational planning. Many weather models now produce forecasts of up to 10 days with reasonable skill, and some can run ensemble forecasts that cover many possible weather paths.

There is also a question of access. If a grid operator relies exclusively on a commercial AI model, it may become dependent on data from outside the organization. That is why many utilities prefer to test new models against their own historical records and in-house forecasts before integrating them into critical systems. Google may need to offer not only the model itself but also interfaces that allow grid operators to connect it to their SCADA systems, energy management software, and market platforms.

Capturing value from renewable forecasting

The biggest payoff from AI weather forecasting is probably in renewable energy integration. In many regions, wind and solar now make up a large share of generation capacity. California, Texas, Germany, and Spain have days when renewables exceed demand, but they also have days when output falls sharply. Forecasting those drops is one of the most important ways to make renewable grids stable.

WeatherNext 3 could help solar plant operators anticipate the movement of cloudy systems and help wind farm operators identify periods of high wind speed that support the safe operation of turbines. Better forecasts also allow operators to plan maintenance around weather. If a model predicts calm conditions for the next 24 hours, a wind farm can take a turbine offline for lubrication or blade inspection without worrying about losing generation.

In power markets, the spread between forecasted renewable output and actual output can have direct financial consequences. Many countries impose imbalance charges when a generator fails to deliver the power it committed to sell. An improved forecast cuts the risk of imbalance by giving the trading desk more confidence in its offer. Even a one-percentage-point improvement in forecast accuracy can be worth millions of dollars across a large portfolio of plants.

Challenges and limitations

AI weather forecasting is not a perfect replacement for physics-based modeling. Machine learning models are trained on data from the past, and weather patterns are evolving because of climate change. If a formerly rare event becomes common, a model may not handle it well. Training data errors can also be absorbed and reproduced by

Source: AI News News

'Our return volumes have been climbing faster than outbound orders, and the flow is increasingly uneven,' a senior operations manager said. 'AI route optimisation is not just a convenience; it is how we keep returns economically viable while maintaining tight delivery windows for shoppers.'

Key facts at a glance

• MG Ship has rolled out AI route optimisation for its returns network.
• Return logistics volumes are accelerating because of e-commerce growth, easy returns and seasonal peaks.
• The system updates routes continuously using traffic, weather, order volume and vehicle capacity data.
• The initiative aims to cut mileage, reduce fuel costs, accelerate refunds and improve asset utilisation.
• MG Ship is applying the same technology used in forward deliveries to the less predictable return flow.

Why returns have become a logistics priority

For many years, reverse logistics was an afterthought. Retailers would accept returned goods, place them in a storage area and wait until a carrier had spare space to move them. That changed as online shopping moved into categories where fit, style and personal taste matter. Apparel, footwear and accessories now drive high volumes of returns, and furniture and electronics are adding heavy and bulky parcels that must be handled with special care.

The acceleration is also driven by changing consumer expectations. A retailer that takes two weeks to refund a returned item can lose a customer to a competitor that processes the refund in two days. In this environment, the speed of the return shipment is as important as the speed of the original delivery. MG Ship's AI route optimisation tackles the first mile of returns: the pickup from a customer's home or at a drop-off location. It also controls the middle mile between consolidation hubs because grouping shipments by projected arrival time allows warehouses to prepare restocking capacity.

How AI route optimisation works

The system starts with a simple question: where are the parcels that need to be picked up and what is the deadline for every stop? It then combines that information with vehicle capacity, drivers' shift times, current traffic conditions and known bottlenecks. A machine learning model weighs all these variables and produces a route that minimises total travel time while respecting service windows.

What makes the new system different is its ability to recalculate throughout the day. Traditional routing tools generate a fixed plan in the morning, and any new return request that arrives after the plan is built is pushed to the next day. MG Ship's platform creates a rolling route. When a customer schedules a return, the system checks whether any vehicle can still visit that address within the requested time frame. If it can, the route is adjusted automatically. Drivers receive an updated stop list through a mobile app, and the warehouse is notified about the expected inbound load.

The platform also coordinates backhauls. A vehicle that has completed its forward deliveries is assigned return pickups on the way back to a depot. This technique, common in less-than-truckload freight, is unusual in final-mile e-commerce routes because of the complexity involved. MG Ship says the AI engine makes it possible to merge these flows without compromising delivery times.

Benefits for retailers and consumers

For retailers, the most visible benefit is the reduction in the time between a customer handing a parcel to a courier and the inventory being available for resale. Faster returns mean less money tied up in stock, fewer clearance markdowns and better availability for other shoppers. The improved data flow also gives retailers a clearer view of return trends, allowing them to adjust purchasing and pricing decisions.

Consumers benefit from transparent scheduling and faster refunds. Many return journeys used to involve a long wait for the customer to be home followed by a trip to a central sorting centre. MG Ship's route optimisation tightens the pickup window and increases the probability that the first collection attempt is successful. If a driver is delayed, the system can alert the customer and propose a revised window. Lower failed-collection rates reduce repeat trips, which lowers costs and brings down the carbon footprint of each returned item.

Cost and sustainability impact

Fuel consumption in urban delivery networks is directly linked to vehicle miles travelled and idle time. By reducing empty miles and cutting out unnecessary depot returns, AI route optimisation has a measurable environmental benefit. MG Ship estimates that more efficient planning can take a significant share of mileage out of its network without affecting service quality. While the company has not published exact figures, it expects the technology to contribute to its long-term target of reducing emissions across its contracted carrier fleet.

There is also a cost-saving angle for carriers. Labour, fuel and vehicle maintenance are the main drivers of every parcel delivery. When routes are balanced more evenly, drivers are not sent to remote areas two times within an hour with a single parcel each visit. The AI engine performs network-wide clustering, which reduces overtime and even allows some routes to be served every other day. These efficiencies matter because returns freight often carries a lower margin than outbound delivery. Without tight route planning, collection rates do not cover the cost of the trip.

The broader shift in logistics returns

MG Ship's deployment is part of a wider movement across the logistics industry. Companies that used to think of returns as a cost centre are now finding ways to turn the reverse supply chain into a source of customer insight and operational efficiency. Artificial intelligence has moved from customer-facing chatbots and warehouse automation to the decisions that happen on the road. Route optimisation is one of the most mature use cases because the data inputs are widely available and the savings are easy to quantify.

At the same time, return rates are unlikely to fall. Retailers may tighten return windows or add fees, but many are hesitant to do so because generous returns are linked to sales growth. For logistics providers, the challenge is not whether to accept returns but how to move them in the same disciplined way as outbound shipments. MG Ship's new AI capability positions the company to deal with the next surge in return volumes while keeping network costs under control.

The company says it will continue to add data sources to the optimisation engine, including predicted return demand based on seasonal trends, weather-related disruption and regional consumer behaviour. As the volume of returns becomes less cyclical and more persistent, the ability to plan every journey in real time is becoming a competitive necessity. Logistics providers that fail to integrate AI into their return networks will struggle with higher costs, more failed pickups and slower inventory replenishment.

Source: AI News News

The XM4 first arrived in the fall of 2020, at the height of a fierce noise-canceling rivalry between Sony and Bose. Each generation of flagship headphones seemed to push the standard higher, with improved noise rejection, richer sound, and more useful smart features. When Sony released the WH-1000XM4, it did not radically change the formula of its predecessor, the XM3. Instead, it refined nearly every aspect of the experience. The result was a pair of headphones that many reviewers considered the best in their class for years. Its combination of performance, comfort, and portability helped it develop a loyal following and stay relevant long after newer models arrived.

By the time the XM5 appeared in 2022, Sony had moved away from the compact folding shape that defined the XM4. It was a deliberate effort to offer a different industrial design. The newer XM6, however, returned to a folding design, though not quite as compact as the one found on the XM4 and XM4C. With the XM4C, Sony appears to be leaning into nostalgia while also filling a gap for listeners who want a straightforward, portable pair of noise-canceling headphones.

A Familiar Look With a Few Small Updates

The XM4C look identical to the original XM4. They share the same hinge design, the same cushioned earcups, and the same ability to fold up into a compact shape that slips into a backpack or travel bag. The new model is available in the standard white and black finishes, plus a new lavender option that has never been offered on the XM4 series before. From a distance, almost no one will notice a difference between the 2020 model and the refreshed version.

Underneath, there are some changes. The XM4C use Sony's newer sound tuning, which brings the tonal balance in line with the current XM6 series. The Headphones Connect app now includes a 10-band equalizer instead of the older five-band equalizer found on the original. There is also a gaming preset, which adjusts the sound for in-game audio cues and may appeal to people who want one pair of headphones for both travel and casual gaming.

The biggest technological upgrade is Bluetooth 6, replacing the Bluetooth 5.0 standard used by the original XM4. This newer wireless version improves connection stability and may help with multipoint features, though Sony has not released specific details about latency or range improvements. In normal everyday use, the experience will likely feel similar to what XM4 owners remember, but with a slightly more modern wireless foundation.

Not everything has changed for the better. The XM4C still rely on the same QN1 processing chip and four microphones for active noise canceling that were inside the original model. That was a flagship setup in 2020, but Sony has moved forward in the years since. The XM5 kept the QN1 but used eight microphones for ANC. The XM6, which arrived with a new QN3 chip, uses 12 microphones. That means the XM4C will almost certainly lag behind the best ANC performance available today, especially when it comes to canceling voices and higher-frequency noises. The question is whether that matters to the target buyer, who may value the classic design and lower price more than the absolute quiet.

Aside from the equalizer, the physical experience should be familiar. The XM4C retains the same comfortable earcup shape, the same plush padding, and the same intuitive touch controls. For longtime fans of the XM4, this is part of the appeal. It was a headphone that did not try to reinvent the wheel. It simply delivered the fundamentals with polish, and many listeners have kept using their original XM4 units long after newer Sony flagships appeared.

Battery and Price Considerations

Battery life on the XM4C is rated at up to 27 hours with active noise canceling turned on. Curiously, that is slightly less than the original XM4, which offered up to 30 hours under the same conditions. The difference is not huge, and most listeners will still be able to fly across the country or wear the headphones through a full workweek before needing to recharge. Still, it is an unusual downgrade in an era when more efficient chips usually translate into better stamina.

The XM4C will be available later this fall for $300. That is $50 less than the original XM4 cost at launch and $100 less than the current XM6. But it is also higher than the deep discounts customers could find near the end of the XM4's original run, when the headphones often sold for around $250 or less. Sony's decision to price the reissue at $300 suggests that it sees the XM4C as a premium product rather than a budget special. It puts the new model in direct competition with the XM5, which is still on sale and currently has an asking price of $200, according to current listings. That price may simply be part of an effort to clear existing XM5 inventory before the arrival of the XM4C, though it makes the old flagship a tempting alternative.

For fans of the original, the XM4C is a way to get a near-identical pair of headphones with a handful of modern adjustments. For someone who already owns a working XM4, it is unlikely to be worth an upgrade. The XM4C will only make sense to new buyers who missed out on the XM4 the first time and want the same kind of comfortable, foldable design without paying the upper-tier price that Sony charges for its latest flagships.

New Midrange and Entry-Level Models

The XM4C is not the only news from Sony's headphone lineup. The company also introduced the next generation of two popular budget and midrange lines: the WH-CH730N and the WH-CH530. Both of these headphones receive the same equalizer updates as the XM4C, including the 10-band EQ and the gaming preset, so even budget buyers get access to the newest app features.

The WH-CH730N sit in the middle of Sony's lineup. They offer active noise canceling and now fold into a more compact shape, making them easier to carry than previous CH700-series models. Sony claims the CH730N can run for up to 50 hours with ANC enabled, which is an impressive figure for midrange headphones. They are priced at $180 and come in black, blue, oat, white, or pink.

The WH-CH530 are more basic. They do not have active noise canceling at all, but they offer battery life up to 55 hours on a single charge. Their entry-level price is $70, and they are available in six colors: black, cobalt, mint green, white, pink, and coral. Both the CH730N and CH530 are available now, which gives Sony's lower-priced lines a quick refresh while the XM4C waits for its autumn launch.

One notable addition to the Sony app is a new safe mode that lets users limit the maximum volume output of the headphones. It is a welcome feature for parents who want to protect their children's hearing when they use wireless headphones. However, there is no way to lock the limit once it has been set, so a determined child could simply go into the app and bypass it. The feature is still useful for casual households, but not a strict parental control.

Hello Kitty Special Edition Bundles

Sony is also celebrating its headphone lineup with a Hello Kitty collaboration. The limited

Source: The Verge News

Welcome to what some in the industry have dubbed “chipflation” or “RAMageddon.” After decades of steadily falling memory prices that made consumer electronics more powerful without making them dramatically more expensive, the trend has abruptly reversed. Corporate transcripts now contain frequent references to memory prices and shortages, reflecting a problem that is pinching every corner of the technology industry, from smartphones and laptops to game consoles and AI servers.

The AI connection isn't the whole story

The obvious culprit is artificial intelligence. AI's rapid expansion has created enormous demand for memory chips, both in data centers and in consumer devices that now run on-device AI models. But the shortage was already brewing before ChatGPT took the world by storm. AI simply poured gasoline on a fire that memory manufacturers had been struggling to contain for years.

The current market is the result of a complicated and highly profitable reshuffling of the memory industry. For years, manufacturers could increase output by packing more chips onto each silicon wafer through technological advances. But those gains began shrinking and taking longer to achieve. By 2021, executives at Micron, one of the three largest memory makers, concluded that technology alone would no longer generate enough capacity to meet long-term demand. The industry needed to build massive new fabrication plants.

Then the memory market collapsed. Pandemic-era demand for computers, tablets, and phones had pulled future sales forward, and when consumer spending weakened, manufacturers were stuck with excess inventory. They lost money, cut production, and slowed their expansion plans. Just as the market began recovering, generative AI ignited a wave of demand far larger and more memory-intensive than anyone had anticipated.

Three companies control nearly all memory supply

The memory business is extraordinarily concentrated. Samsung, SK Hynix, and Micron together control about 90 percent of the global memory market, according to estimates from Counterpoint. In the second quarter of 2026, Samsung held roughly 39 percent, SK Hynix had 26 percent, and Micron had 25 percent. That leaves the entire world dependent on a handful of companies deciding how to allocate limited capacity between AI infrastructure and consumer electronics.

Memory inside phones and computers generally falls into two categories. DRAM, or dynamic random-access memory, provides temporary storage for running applications and loading data. NAND flash memory offers longer-term storage for photos, files, and software. Both are in short supply, but DRAM has become the more critical bottleneck.

AI data centers need a special form of DRAM called high-bandwidth memory, or HBM. Unlike the DRAM in your phone, HBM is designed to move enormous quantities of data at blazing speeds, which is essential for training and running complex AI models. It is made by stacking memory chips together with advanced packaging, and it consumes significantly more manufacturing capacity than conventional DRAM.

“It's not as simple as saying data centers are consuming RAM,” says David Naranjo, an analyst at Counterpoint. “The RAM is not the same.”

Micron estimates that producing a given amount of HBM requires roughly three times as many wafers as producing the same amount of conventional DRAM. HBM is also more lucrative to sell, making it easy for manufacturers to prioritize AI customers like Nvidia, AMD, Meta, and Microsoft, who are willing to sign multiyear commitments and pay premium prices.

“The three big memory guys are basically allocating the capacity that they have to these companies,” Naranjo says.

Why this means higher phone prices

As memory makers shift capacity toward HBM, less conventional DRAM is available for phones, PCs, and other consumer devices. At the same time, those devices need more memory than ever because they are increasingly running AI features directly on-device, which requires larger and more sophisticated DRAM configurations.

The numbers are staggering. Counterpoint estimates that smartphone DRAM prices jumped about 56 percent in the first quarter of 2026, and then surged another 83 percent in the second quarter. For a 16GB DRAM module used in a smartphone, the cost went from roughly $42 in the second quarter of 2025 to about $181 a year later, an increase of more than 300 percent.

Not every company pays those prices. Apple has extraordinary leverage with suppliers and may negotiate better terms. But the figures illustrate how dramatically component costs have changed across the industry.

The impact is already visible. Microsoft recently raised Xbox prices by another $100 to $150, leaving some models as much as $300 more expensive than at launch. It also increased prices on some Surface laptops and tablets by $500 over their original starting prices. Meta added $100 to the price of its Quest 3 headset. Samsung and Google have each raised the starting prices of some new phones by $100 this year.

Apple faces its biggest test yet

Apple is perhaps the most revealing case study because it should be best positioned to weather the storm. Its scale gives it enormous leverage with suppliers, and its customers have proven willing to pay premium prices. Yet Apple has already raised prices across its Mac and iPad lineups, and now the iPhone is under pressure.

Rather than launching its full lineup at once this fall, Apple is expected to introduce the iPhone 18 Pro and Pro Max alongside its first foldable iPhone, while holding the standard iPhone 18 and a refreshed Air model until spring 2027, according to recent reports. That concentrates its fall releases at the most expensive end of the market, exactly where there is the most room to protect margins.

Counterpoint's supply-chain tracking shows Apple preparing roughly 10 percent more iPhone 18 Pro and Pro Max units for September through December than it allocated to the equivalent iPhone 17 models during the same period last year. On Apple's most recent earnings call, then-CEO Tim Cook described memory pricing as a “100-year flood,” citing exponential increases that forced the company to raise prices.

The iPhone 18 Pro could start at $1,299, according to calculations from TechInsights cited by The Wall Street Journal. That would be $200 more than the iPhone 17 Pro. Analysts suggest Apple may be able to pull off these higher prices because its phones behave more like luxury goods than interchangeable electronics. The company has already gained market share by keeping prices steady while lower-end competitors raised theirs.

New fabs are coming, but very slowly

The obvious long-term solution is to make more memory. But building a new chip factory takes years, even under the most accelerated timelines.

Micron is currently constructing a massive manufacturing complex near Syracuse, New York. The site, once completed, will contain 2.4 million square feet of cleanroom space, making it the largest semiconductor manufacturing site in US history and Micron's biggest facility anywhere in the world. Yet the cleanrooms will occupy only a fraction of the campus, which will span the size of roughly 350 football fields once fully built.

The complexity is difficult to overstate. Micron executives describe the challenge of keeping microscopic particles off wafers as akin to building all of New York State without allowing a single ant anywhere in the entire territory. The facility requires enormous systems for water treatment, power distribution, air handling, and vibration control, all working together in perfect precision.

Micron broke ground in January 2026 and poured the first concrete in July, ahead of schedule. But essential systems won't be fully activated until the end of 2028, and the company doesn't expect meaningful output until 2030. Even an accelerated timeline means more than six years from permitting to production. The company's new Idaho fab is further along and is expected to begin wafer output in mid-2027.

No relief until 2028 at the earliest

Micron has roughly doubled its capital expenditure this year, exceeding $25 billion, but it cannot identify a clear point when supply will catch up with demand. Demand continues to grow almost as quickly as manufacturers can expand production. Micron executives say the shortage will extend beyond 2027, with no clear end in sight.

Counterpoint expects capacity to catch up with demand in the best-case scenario by late 2027 or early 2028. IDC similarly forecasts the shortage will last through 2027 and well into early 2028 before meaningful relief arrives. But relief will likely not mean a return to old prices.

“Not to say prices will come down to 2025 levels, but the price increases will stabilize at a new normal,” says Nabila Popal, a senior director at IDC. That new normal, she adds, is “at least triple what they used to be.”

What happens next

PC and smartphone manufacturers cannot absorb component costs that have multiplied several times over indefinitely. They can raise prices, reduce device counts, include less memory, or focus exclusively on high-end models where profit margins are large enough to absorb the added cost. Industry analysts are already seeing smartphone companies reduce expected shipment volumes while shifting toward more expensive models with premium features.

This means more OLED displays, larger storage configurations, advanced camera systems, and other upgrades designed to justify higher prices. As a result, industry revenue could remain flat or even rise while unit sales decline. The memory shortage isn't just making electronics more expensive; it is reshaping which electronics companies choose to build in the first place.

Samsung, SK Hynix, and Micron are all investing heavily in additional capacity. SK Hynix has accelerated the completion target for its massive Yongin Semiconductor Cluster from 2045 to 2033 and says it will double capacity within five years, yet even that may not keep pace with AI-driven demand. Samsung and SK Hynix recently announced plans to invest a combined 800 trillion won, roughly $588 billion, in four new memory factories in South Korea.

A potentially important fourth player is emerging in China. CXMT is building additional fabrication plants, though its technology lags behind the three dominant manufacturers. Apple has reportedly been testing CXMT chips, but would need approval from the Trump administration to do business with the company on a large scale.

The other possibility is that demand collapses before supply catches up. If the AI investment bubble bursts, memory manufacturers would suddenly face a very different competitive landscape. But for now, long-term contracts signed by AI companies with memory suppliers make the immediate future relatively predictable. As one analyst put it, “2027 is pretty locked and loaded.”

Yet even if price increases slow from their current breathtaking pace, they are unlikely to reverse. New fabs take years to come online, AI data centers continue to expand, and consumer devices require ever more memory. Memory manufacturers have little economic incentive to prioritize cheaper consumer electronics when they can lock in massive contracts from the world's wealthiest companies. The era of ever-cheaper, ever-more-powerful gadgets may be over for the foreseeable future.

Source: The Verge News

What is recurrent depth?

Under normal circumstances, a reasoning model produces a chain of thought: a sequence of intermediate steps showing how the model works through a problem. These logs are not perfect reflections of the model's internal calculations, but they provide valuable signals about whether the model is reasoning safely. If the logic drifts into something deceptive or misaligned, chain-of-thought records can help researchers understand why.

Opaque recurrence changes that picture. Instead of following a linear path from question to answer, the model processes the same query several times in a loop. That repeated processing happens in a more compressed, latent space and produces fewer legible traces for monitors to inspect. The observable chain of thought may capture only the beginning and end of the process, with the intermediate steps hidden inside the model's opaque computations.

OpenAI's use of the technique in Astra is said to be limited. The company reportedly expects the model's chain of thought to remain largely legible, and it has denied any plan to switch to "neuralese," a hypothetical shorthand that would make model reasoning impossible for humans to follow. Still, the fact that recurrent depth is being used at all has become a major flashpoint in the AI safety community.

Why safety experts are concerned

AI safety researchers have spent years arguing that chain-of-thought monitoring is essential for understanding and controlling powerful models. Regulators and oversight groups have also come to rely on the idea that models can explain their reasoning. If labs begin using techniques that make those explanations opaque, safety researchers fear the entire ecosystem loses a critical defense.

Buck Shlegeris, CEO of the Redwood Research safety group, said he was "extremely concerned" by the reports about Astra and opaque recurrence. He noted that he did not know whether Astra is much less monitorable than previous models. But he warned that if OpenAI pushes the technique further, the company will have the option to massively increase recurrence and "totally destroy CoT monitorability."

Longtime AI safety advocate Zvi Mowshowitz also weighed in, suggesting that laws might be needed to prevent a "race to the bottom" among AI labs. Mowshowitz described the technique as "playing with fire," arguing that OpenAI and Anthropic had fought to establish a norm that labs should work hard to maintain chain-of-thought faithfulness and monitorability for as long as possible. More intensive use of opaque recurrence, he warned, would probably damage that monitorability.

How chain-of-thought monitoring became a safety tool

Chain-of-thought records gained prominence as large language models became increasingly capable of multistep reasoning. Rather than simply predicting the next word, modern reasoning models are trained to produce visible steps that look like deliberation. Those logs can be used by safety teams to audit whether a model is following instructions, resisting manipulation, or concealing harmful goals.

In some high-profile incidents, chain-of-thought records proved essential. When OpenAI's rogue agent activity made headlines, researchers used chain-of-thought logs to tease out why certain autonomous agents behaved the way they did. The records helped show whether failures were caused by misinterpreting instructions, reacting badly to unexpected inputs, or something more fundamental.

But chain-of-thought has always had limits. All AI models perform some amount of opaque reasoning; the visible text is not a direct transcript of the model's hidden computations. It is better understood as a rough explanation generated during the reasoning process. That caveat has not stopped safety researchers from treating chain-of-thought as one of the most important tools available.

OpenAI defends its approach

OpenAI has pushed back against the idea that Astra represents a departure from its safety commitments. Chief scientist Jakub Pachocki said on social media that the lab has worked to preserve and utilize chain-of-thought monitoring since its first reasoning models. He added that maintaining legible chain-of-thought is a core goal of the company's current research program.

The company has also announced plans for extensive chain-of-thought monitoring systems as part of its forward-looking safety plans. Those plans are intended to demonstrate that OpenAI is taking alignment seriously, even as it explores new architectures and reasoning techniques.

Critics are not entirely mollified by such assurances. They point out that a technique may start limited and then be scaled up as competitive pressure grows. If one lab benefits from faster, more capable opaque reasoning, other labs may feel forced to follow. That dynamic is what Mowshowitz called a "race to the bottom."

Industry adoption and broader concerns

The concern is not limited to OpenAI. A follow-up report on Wednesday morning said that Anthropic and Google DeepMind have already discussed similar techniques. That suggests the interest in opaque recurrence may be spreading across the frontier of AI research, which safety experts see as dangerous.

Ryan Greenblatt, chief scientist at Redwood Research, wrote in response to the news that opaque reasoning could easily scale faster than conventional chain-of-thought reasoning. In that scenario, all meaningful reasoning would eventually move into hidden channels, leaving monitors with only superficial outputs. "My biggest concern is that a natural progression from here would involve scaling up the opaque reasoning to the point where the model reasons entirely or almost entirely in latent space," Greenblatt wrote. "I hope it isn't too late to avoid the most concerning architectures and that OpenAI will stop here."

Greenblatt's reference to latent space points to a deeper issue. As models become more powerful, they may naturally develop compressed internal representations that are far more efficient than human-readable text. If researchers force models to reason in natural language, they may sacrifice performance or create misleading explanations. But if they allow models to reason opaquely, they may lose the ability to inspect dangerous thought processes.

Historical context and policy implications

The debate over opaque recurrence echoes earlier battles in AI safety. When OpenAI and other labs moved from simple language models to chain-of-thought reasoning systems, some researchers argued that the visible reasoning was merely a convincing fiction. Others believed it could be made faithful enough to serve as an honest window into the model's goals.

That debate is still unresolved. However, safety-minded labs have generally agreed that trying to maintain legible reasoning is worthwhile, even if the representations are imperfect. They have built monitoring systems that flag inconsistencies, hidden intentions, and suspicious jumps in logic. Opaque recurrence threatens to bypass those systems entirely.

Policy responses will likely be complicated. If the concern is that labs will knowingly sacrifice monitorability for performance, regulators may need to establish minimum standards for model transparency. Some experts have suggested requiring labs to demonstrate that their models produce meaningful chain-of-thought records before being deployed. Others have argued that formal technical standards are premature until researchers better understand how opaque recurrence affects actual behavior.

There is also a question of measurement. How much opacity is too much? If Astra's use of recurrent depth is limited, as reported, it may still pass any reasonable standard for monitorability. But safety experts worry that the threshold could be crossed quietly as later models adopt deeper recurrent loops. By the time regulators detect the shift, they argue, the most dangerous models may already be deployed.

What happens next

OpenAI has not suggested that Astra will be the last model using recurrent depth. The success of the technique with Astra will likely determine whether it appears in future systems. If recurrent depth improves performance or efficiency, other labs may adopt it despite safety concerns.

The research community is now focused on whether monitors can be developed for opaque reasoning. One possibility is that scientists will find ways to probe latent space more effectively, turning invisible reasoning into something that can be audited after all. Another possibility is that models will be trained to explain their decisions even when those decisions were not made through an explicit detectable chain.

But such techniques are uncertain, and experts like Greenblatt worry that they will lag far behind the development of more advanced models. The history of AI safety has often been a game of catch-up, with interpretability tools struggling to keep pace with scale and capability.

For now, the debate centers on whether OpenAI will treat recurrent depth as an experiment or as a foundation for future development. The company's public statements emphasize caution, but its actions will matter more. Safety researchers are watching closely, hoping that visible reasoning remains a standard part of the industry.

As Mowshowitz observed, the technique touches a taboo that safety-minded labs have worked to establish: the commitment to keeping chain of thought as honest and monitorable as possible. That commitment is now under pressure from one of the most powerful players in the field.

Source: TechCrunch News

The lawsuit is one of the most closely watched copyright disputes in the AI industry. The Times alleges that OpenAI copied millions of its articles without authorization and used them to train ChatGPT and other AI systems. In its recent brief, the administration does not explicitly decide whether OpenAI violated copyright law. Instead, it argues that the broader legal standard for fair use must not be applied in a way that would impede innovation in artificial intelligence.

Government’s core argument

The brief frames the case as not merely a dispute between two private parties, but as a matter of national importance. It argues that the United States has a strong national interest in maintaining a thriving AI sector and that American AI companies must remain competitive with rivals around the world. The administration points to an executive order signed by President Trump last year that called for retaining global leadership in artificial intelligence as an official policy objective.

“Constraining LLM development under a misunderstanding of fair use doctrine would thwart such creative and scientific progress while hindering American prosperity and economic mobility,” the government brief states.

This framing suggests that the federal government is less interested in resolving the specific facts of the lawsuit and more concerned with establishing legal precedent that keeps AI companies free to train their models on large bodies of text without facing routine legal liability. The brief also warns that a ruling against OpenAI could ripple across the entire artificial intelligence ecosystem, affecting not only massive foundation model developers but also startups, researchers, and downstream products that rely on large language models.

The fair use question

At the center of the case is the legal doctrine of fair use, which allows limited unauthorized use of copyrighted material under certain circumstances. Courts generally examine four factors when deciding fair use claims: the purpose and character of the use, the nature of the original work, the amount used, and the effect on the original work’s market.

AI companies have argued that using copyrighted books, articles, and other texts to train neural networks qualifies as transformative use. Their key argument is that AI models do not reproduce the original works wholesale. Instead, the training process extracts linguistic patterns, semantic associations, and broader knowledge from millions of examples. According to this view, the resulting model is a new creation, not a substitute for the originals.

Publishers and writers reject that reasoning. The New York Times and other copyright holders argue that “training data” is simply unpaid copying on an unprecedented scale. They have also expressed concern that AI chatbots can sometimes quote, paraphrase, or summarize copyrighted works in ways that compete directly with the original publishers. Those outputs, they argue, pose a real commercial threat to newsrooms and authors who depend on subscription and royalty income.

The fair use debate in AI copyright cases is therefore not purely academic. It determines whether AI companies can continue operating their current training pipelines with relative freedom, or whether they must rebuild those pipelines through expensive licensing agreements, opt-in systems, or restricted data sets.

A legal environment increasingly favorable to AI companies

The government’s intervention comes after several high-profile lawsuits produced at least partial wins for AI companies. In one prominent case brought against Anthropic, a group of fiction writers accused the company of ingesting their books to train its Claude models. Last year, federal judge William Alsup ordered Anthropic to pay $1.5 billion in a copyright settlement. But the crucial detail in that case was narrow: Anthropic was not penalized for using copyrighted books to train its models as such. Rather, the company was punished for obtaining many of those books from illegal shadow libraries that hosted pirated copies. Judge Alsup explicitly drew a distinction between the act of reading for training and the unlawful gatekeeping used to acquire the source texts.

In his opinion, Alsup compared Anthropic’s language model training to a human writer reading published works. “Like any reader aspiring to be a writer, Anthropic’s LLMs trained upon works not to race ahead and replicate or supplant them — but to turn a hard corner and create something different,” he wrote.

That analogy has become a common talking point among AI advocates. They argue that if a human is allowed to read a novel and learn from its style, plot construction, and vocabulary, then an artificial intelligence should similarly be allowed to learn from texts without paying a separate licensing fee. Opponents respond that machine learning operates at a scale no human can match, and that ingesting millions of copyrighted books and articles is qualitatively different from a writer reading a few volumes in a library.

Other cases have produced mixed results. A class action brought by writers against OpenAI over its use of copyrighted books was partially dismissed, but the judge allowed some claims to proceed. A separate lawsuit against Meta over the training of its Llama models has also continued through the courts. Meanwhile, several visual artists have sued AI image generators such as Stability AI and Midjourney over the use of their artwork in training data sets. Some of those lawsuits have been narrowed, while others have survived initial motions to dismiss.

The broad legal picture, then, is still unsettled. But the trend so far has favored the idea that merely training AI on copyrighted material does not itself constitute infringement. The Trump administration’s brief continues that pattern at the executive level, signaling that the government may be reluctant to support legal theories that would force AI companies to pay retroactive royalties on all their past training data.

The significance and limitations of the government brief

It is important to note that the Trump administration’s brief is not a court ruling. The case is being tried in the U.S. District Court for the Southern District of New York, and the brief’s authors do not have authority to decide the outcome. Judges are not obligated to follow the views of the executive branch when interpreting copyright law.

Nor is the brief binding on other federal courts, where several similar copyright disputes are pending. Each case will ultimately hinge on its own facts, the location of the court, the specific AI system in question, and the evidence presented about how training data was collected and used.

Still, the government’s intervention carries rhetorical weight. Courts sometimes look to the executive branch for guidance on matters of national policy, particularly when a decision could have broad economic and geopolitical consequences. If this case reaches an appeals court or the Supreme Court, the Solicitor General could make similar arguments, potentially elevating the issue to the highest level of the judiciary.

The brief also adds to a growing record of federal government attention to AI. Since the release of ChatGPT, lawmakers have introduced a wide range of AI-related bills addressing subjects such as election deepfakes, algorithmic discrimination, and national security risks. But funding bills and executive orders are not the only tools shaping AI policy. Filings like this one in private litigation represent another pathway through which the government influences the rights and limits of AI companies.

Implications for publishers and the broader news industry

For The New York Times and other news organizations, the government’s decision to side with OpenAI is a significant political setback. They have argued that journalism is expensive to produce and that AI companies are effectively taking the value of that journalism without compensating the people who wrote and edited the stories. News industry trade associations have filed their own briefs in various cases, urging courts to recognize the real harms that unpaid AI training data can cause to publishers and to the public’s access to reliable information.

Some publishers have chosen an alternative path by signing licensing deals with OpenAI and other AI companies. The Associated Press, Time, News Corp, and several other outlets have reached commercial agreements that permit their stories to be used in AI training. These deals are sometimes described as a pragmatic way to create a new revenue stream amid ongoing uncertainty over the law. But not every news organization can easily strike such agreements, and smaller outlets may be left without the legal or business leverage to negotiate favorable terms.

The administration’s brief is unlikely to be the final word on whether AI companies need permission before training on copyrighted material. But it does signal that the federal government’s executive branch is prepared to defend a permissive legal environment for AI development. That stance may shift the broader public debate even as judges continue to weigh the details of individual cases.

As the New York Times lawsuit and other copyright proceedings move forward, the central question remains unresolved: whether AI companies can continue to absorb the published knowledge of humanity without paying every copyright owner whose works are used in training. The answer to that question may ultimately depend on how courts interpret the transformative purpose of large language models and how much importance they assign to American leadership in artificial intelligence.

Source: TechCrunch News

The core question for users is how much control they are willing to give a language model over their digital life. Andrew Ambrosino, lead engineer for OpenAI’s desktop app, has already handed over access to his inbox, Slack, phone, Notion, Figma, and other tools. He understands the risk: the model could pull from a private DM or share information it should not, especially if asked to write a document. Nevertheless, he says the reward outweighs the potential embarrassment, and he has not yet lost that bet.

ChatGPT Work is an adaptation of Codex, OpenAI’s coding agent. Codex demonstrated that artificial intelligence can handle complex software engineering tasks when given access to a command line and the right permissions. ChatGPT Work expands that capability beyond software developers. It is designed for accountants, investors, doctors, operations managers, and anyone else whose daily work is dominated by a computer. Thibault Sottiaux, OpenAI’s product leader for the effort, says the model can complete very complicated tasks autonomously and safely. He describes it as the fulfillment of OpenAI’s founding mission: giving everyone access to advanced machine intelligence, not just programmers.

The commercial reasoning is straightforward. An agent that works for hours instead of a few minutes will consume far more tokens, making each subscriber more valuable. OpenAI must reach professions beyond coding, however. Software engineering is a relatively small slice of the white-collar workforce. Meanwhile, specialists such as Harvey, which builds legal AI tools, and Clay, which sells sales workflow tools, are already pursuing those vertical markets. They use whichever model performs best for a given client, which means they do not necessarily need to buy model access from a single large lab.

That dynamic has prompted analysts to warn that OpenAI and its competitors will not automatically capture the value of the AI boom. If the labs cannot control the full workflow, from model to interface to business process, value may shift to companies that own the customer relationship and understand domain-specific needs. ChatGPT Work is partly a response to this threat. By controlling the harness — the software that wraps around the model, manages context, and invokes tools — OpenAI keeps itself close to the user.

A step beyond coding

The internal adoption data suggests why OpenAI needed a product like ChatGPT Work. An OpenAI-backed study found that in June, 98 percent of OpenAI employees used Codex. Among organizational subscribers, only 17 percent had used it, and less than 1 percent of individual subscribers had. That gap between internal and external adoption is the clearest possible indication that coding tools do not naturally appeal to non-engineers.

One reason is the interface. The first versions of Codex were unwelcoming to people who are not programmers. Non-coding employees inside OpenAI encountered messages about empty diffs and other technical terminology. The team spent months making the tool more general purpose, teaching it to understand natural language prompts rather than requiring users to think in code.

Many non-technical users have never touched a command-line interface, and that is fine. A mainstream agent needs to work with the messy world of existing software: web pages built in the 1990s, half-integrated SaaS apps, permission systems, and legacy tools. Ambrosino’s team believes the product experience itself is the key to bringing people along. He compares early agent interfaces to skeuomorphism, the practice of making digital tools resemble physical objects. That approach may look unnecessary over time, but it helps newcomers cross the psychological gap between using software and commanding an AI.

The current product has more buttons than a pure conversational interface. Users can select projects, manage plugins, and inspect the tools the model plans to use. OpenAI’s designers defend these extra controls because discoverability matters in the early stages of a new technology. Eventually, as users learn to trust the model, the buttons may disappear.

What people do with the agents

OpenAI pitches ChatGPT Work as a tool for routine, data-intensive coordination. Within the company, employees are using it to generate weekly metrics reports, turn spreadsheets into planning dashboards, and analyze Slack conversations after engineering debates. Outside OpenAI, early adopters report using the product for investment memos, data visualizations, and a wide range of annoying administrative work. Sam Altman is reportedly using it to plan vacations, a task that would require pulling together flights, hotel offers, and restaurant reservations from many sources.

Akshay Nathan, who leads OpenAI’s product engineering team, argues that modern employees are drowning in information stored in systems such as Salesforce and corporate wikis. They cannot parse all of it before acting. ChatGPT Work offers a way to search across dozens of pieces of context and distill them into something useful. This vision is not unique. Anthropic’s Claude Cowork and Perplexity’s browsing agent offer similar capabilities, and the competition is fierce.

The utility is real, but limited by the user’s willingness to grant access. In one detailed test by a journalist, ChatGPT Work successfully extracted a preschool calendar from email and placed every event into Google Calendar, saving considerable repetitive work. The same reviewer asked it to compile an auto-updating dashboard of financial metrics and to make a queryable database of space launches that had previously required writing Python code. The program even sent a weekly digest of new academic AI research. But the reviewer deliberately withheld access to personal inbox, source interviews, and bank accounts, which made the model less effective than it could have been.

The experience is not seamless. Connecting a cloud drive can be confusing, and some crucial settings are found only on the web version. Linking Google Calendar allows the model to create events, but not to create new calendars. With the effort level set too low, outputs feel sloppy, like the work of a careless intern. One OpenAI engineer, Joe Gershenson, admits that effort settings are not yet intuitive for new users and says the company plans to improve the reasoning levels in future updates.

Rivalries and lessons

OpenAI’s engineers are reluctant to discuss Claude Cowork or Claude Code, Anthropic’s high-profile agents. Asked what differentiates ChatGPT Work from those products, they mostly say they focus on their own work. One engineering leader joked about the Mad Men scene in which a character claims never to think about his competitors. The claim is hard to believe, because the products are similar and ChatGPT Work’s signup flow invites new users to port data from Claude Cowork.

Anthropic deserves credit for defining the modern coding agent. OpenAI initially built Codex as a web app that attempted to do too much without user input. Anthropic’s Claude Code, by contrast, asked the user for choices, checked back after each step, and generally kept humans in the loop. That iterative approach worked better, and OpenAI eventually redesigned Codex along the same lines.

The competitive balance shifted after months of complaints from Anthropic users about safety restrictions and compute shortages. OpenAI improved its model, cut prices, and invested in making the harness more flexible. By the spring of this year, Codex and Claude Code were roughly matched in download volume, and enterprise adoption of OpenAI’s agent was accelerating.

The model and the harness

Under the hood, every major AI lab must decide how much engineering should go into the harness. OpenAI’s engineers subscribe to what the industry calls the bitter lesson: a larger, smarter model will beat clever software scaffolding in the long run. Gershenson says that adding many custom rules and tools can improve results for a few weeks, but then a new model arrives and makes those extra parts obsolete. His team therefore tries to expose only the information the model truly needs, avoiding unnecessary constraints.

Not everyone agrees that harness engineering is merely a temporary aid. Third-party evaluations by companies such as Composio and Databricks show that different harness and model pairings produce different benchmark results. Databricks found that Pi, an open-source harness developed by the software company Earendil, outperformed ChatGPT Work’s Codex tool even when it used the same GPT-5.5 model underneath. Pi has also been used to build popular tools like OpenClaw and the Cloudflare OS.

Pi’s creator, Mario Zechner, argues that the big labs promote their own harnesses because they want to own the full product stack. If users can swap in any model, he says, the labs become mere model providers and face brutal competition from lower-cost open-source models. Zechner also sees a fundamental reason that coding agents have advanced farther than general white-collar agents: coding generates abundant training data and provides clear feedback. A decision made by a manager might not show its consequences for months, which makes it harder for a model to learn from the result.

Costs and permissions

One important issue for any independent evaluation is the opaque economics of agentic

Source: TechCrunch News

Alteon, founded by 20-year-old Samay Sanghvi, announced Tuesday that it raised $2.5 million in a pre-seed round led by Groom, with participation from Together Fund. The funding will support the startup's development of small, fixed-wing autonomous aircraft inspired by dynamic soaring, a technique albatrosses use to extract energy from the wind.

According to Sanghvi, Groom decided he wanted to invest within the first 30 minutes of their initial meeting.

Dynamic soaring: The albatross advantage

Dynamic soaring is a flight maneuver that allows an aircraft to gain energy by repeatedly crossing boundaries between air masses moving at different velocities. Albatrosses and other seabirds use this technique to travel vast distances over the ocean with minimal flapping, extracting energy from wind shear above the water's surface.

Conventional aircraft must carry all the fuel or battery power required for a mission, which severely limits endurance. Alteon is trying to break that limitation by designing its autonomous aircraft to use dynamic soaring as a primary energy source. In theory, an aircraft that can efficiently harvest energy from the wind could remain airborne for months or even years at a time.

“Once you build airplanes that can stay in the air for more than a year, there are millions of things you can do with them,” Sanghvi told TechCrunch. Alteon plans to initially use its aircraft for maritime surveillance, giving governments real-time visibility into activity in their waters.

How Alteon's aircraft works

The initial plan is to build an aircraft with around a 3-meter wingspan that flies close to the ocean's surface, climbs, turns through faster-moving air, and repeats the cycle to extract energy from the wind. Eventually, Alteon plans to use its propellers as turbines, converting some of that energy into electricity to recharge onboard batteries.

This approach is radically different from conventional solar-powered or battery-electric long-endurance aircraft, which rely on sunlight or stored energy. Dynamic soaring, by contrast, uses the kinetic energy of moving air directly, potentially enabling flights that outlast traditional power sources.

Alteon has not yet demonstrated sustained flight using energy harvested through dynamic soaring. However, the company completed a recent test of its autonomous flight system over the Bay of Bengal. During that test, the aircraft autonomously completed seven O-shaped cycles at more than 62 miles per hour, all while flying within 1 meter of the water's surface.

Sanghvi said that staying very close to the waves is essential. Wind speeds are slower near the surface and faster at higher altitudes, and the difference between those layers is what dynamic soaring exploits. Flying that low, however, is challenging because the aircraft must avoid waves and cope with the turbulent air created by the ocean's motion.

Expert perspectives on Alteon's progress

Dr. Gabriel Bousquet, a Silicon Valley-based aerospace and robotics engineer who researched dynamic soaring during his PhD at MIT, called Alteon's low-altitude flight over water a “promising first result.” But he noted that the harder challenge is proving the aircraft can reliably extract enough energy from real-world winds to sustain flight for extended periods.

“Flying low enough to harvest that energy safely is particularly difficult,” Bousquet said, because the aircraft must contend with turbulence, waves, spray, rain, and changing light conditions while continuously sensing and reacting to a moving ocean surface.

Dr. Bharath Swaminathan, who earned his PhD from IIT Madras studying the stability of dynamic soaring, said the underlying physics is well established and called Alteon's effort commendable. Keeping an aircraft airborne for several days using dynamic soaring would itself be “a very big step, and a big achievement,” he said.

Still, Swaminathan cautioned that while large-scale wind conditions may be predictable, local wind shear and turbulence can vary substantially, complicating an aircraft's ability to continuously extract energy from the wind. Some of those challenges, he suggested, may only emerge through real-world flight testing.

A young founder with a history of persistence

Sanghvi began working on what would become Alteon straight out of high school in 2023. He taught himself to build aircraft by making and crashing radio-controlled models before developing early prototypes. He formally founded Alteon in 2025 and received early backing from Emergent Ventures and 1517.

Alteon now has a team of 20 in Bengaluru and operates from a 10,000-square-foot facility. According to Sanghvi, the startup is building four to five aircraft a week for testing and has conducted more than 200 test flights in the past 30 days alone.

That rapid iteration has allowed Alteon to work through many practical issues, from control algorithms to airframe durability. Each test flight helps refine the autonomous systems that must eventually handle dynamic soaring without human intervention.

The road to energy-neutral dynamic soaring

Alteon's next major milestone is what Sanghvi calls “energy-neutral dynamic soaring.” This would allow the aircraft to fly continuously with its propulsion switched off, extracting enough energy from the wind to remain aloft. Achieving this milestone would validate the core physics and engineering of the company's concept.

The implications of such a breakthrough extend far beyond maritime surveillance. Aircraft that can remain aloft for a year or more could transform communications relays, environmental monitoring, storm tracking, and disaster response. They could also provide persistent coverage of remote ocean regions where satellites and conventional drones have limitations.

For Lachy Groom, the bet is as much about the team as the technology. Groom, known for early investments in companies like Stripe and a number of frontier-tech startups, has a track record of backing founders tackling extremely difficult engineering problems.

“Ambitious problems are always going to come with risks,” Groom said. “For me, it came down to believing Samay and the Alteon team are the ones to figure them out.”

The aviation industry has long dreamed of aircraft that can stay airborne indefinitely. During the Cold War, the United States developed the Lockheed U-2 and later the SR-71 for high-altitude reconnaissance, but both required massive logistical support and frequent refueling. In recent years, companies have experimented with solar-powered high-altitude pseudo-satellites such as Airbus's Zephyr and Facebook's Aquila, but these aircraft rely on sunlight and heavyweight batteries, which limits their endurance and weather tolerance in certain regions.

Dynamic soaring takes a different path. Instead of fighting the wind, it embraces it. The albatross, with wingspans reaching 11 feet, routinely travels hundreds of miles per day without flapping, using precisely the technique Alteon aims to replicate. Scientists and engineers have studied dynamic soaring for decades, and some radio-controlled glider enthusiasts have used it to keep their models airborne for extended periods, but no one has yet built an autonomous aircraft capable of doing so reliably for days, let alone a year.

One of the reasons has been the sheer complexity of the maneuver. Dynamic soaring requires precise control at high speeds and low altitudes, often within a few meters of the ocean surface. The aircraft must sense its altitude continuously, adjust its flight path many times per second, and do so with very little margin for error. A wave height of a few feet or a sudden gust of turbulence can quickly end a mission.

Alteon's early test flights suggest the control systems are improving. The Bay of Bengal flights demonstrated an ability to hold altitude within 1 meter while maintaining speeds above 62 mph, which is necessary for the wind-shear harvesting maneuver. Sanghvi says the company chose the Bay of Bengal for testing because it offers strong and relatively consistent wind conditions, as well as open water with clear airspace.

The startup is not just working on the flight control system. Materials and energy management are equally important. The aircraft must be light enough to achieve dynamic soaring but strong enough to survive weather and heat. Its sensors must consume very little power, and the eventual propeller-turbine system must efficiently convert wind energy into electricity without adding excessive weight or drag.

Sanghvi acknowledged these engineering challenges but said the team's rapid iteration gives him confidence. “We have learned more in the last 30 days than most aircraft startups learn in a year,” he said. “Every crash teaches us something. Every test changes the next design.”

Beyond the technical hurdles, Alteon will also need to navigate regulatory requirements, especially if it plans to operate aircraft over the oceans for months at a time. Civil aviation authorities have developed rules for drones, but an autonomous aircraft that stays aloft for a year and flies over international waters will likely require new frameworks. For now, Sung backers seem comfortable with the risk.

The $2.5 million pre-seed is modest by aerospace standards, but Alteon has no immediate plans for expensive launches or full-scale demonstrators. Instead, the startup is focused on incremental flight testing and refining the algorithms that make energy-neutral soaring possible.

Groom's involvement may also help Alteon attract follow-on funding as it approaches its demonstration targets. Together Fund, a SaaS-focused venture capital firm, participating in the round signals that investors with a broader startup focus are taking the concept seriously.

For the aerospace research community, Alteon's progress is being watched with both excitement and caution. If the startup succeeds, it would not only shatter endurance records but also open new explorations of atmospheric physics and aeroelasticity. If it fails, the lessons learned will still be valuable.

Sanghvi is unmoved by skepticism. He sees Alteon as a first step in a longer journey toward more autonomous, more capable aircraft that can operate indefinitely in dynamic environments. “The aircraft are just the beginning,” he said. “The systems we build to control them will have applications far beyond what we're doing today.”

Alteon's immediate timeline appears ambitious. The company aims to demonstrate energy-neutral dynamic soaring within the coming months, and its larger goal of full-year endurance remains years away. It remains to be seen whether the physics and engineering will align as neatly as the company expects, but for now the startup is moving quickly from drawing board to ocean.

Source: TechCrunch News

Startup Battlefield has long been a launchpad for breakout companies. Past participants include Dropbox, Cloudflare, and Discord, and the program says its alumni have raised more than $32 billion and produced more than 250 exits. For this year's group, the selection opens the door to live demos, investor meetings, global media exposure, and a head-to-head pitch competition with a $100,000 equity-free prize and the Disrupt Cup on the line.

Key facts about the 2026 Startup Battlefield 200

• Event dates: October 13 through 15, 2026
• Event location: Moscone West, San Francisco
• Announcement date: August 20, 2026 at 9:00 AM PDT
• Number of startups selected: 200
• Top 20 finalists: announced live on Day 1 of Disrupt, October 13
• Grand prize: $100,000, equity-free, plus the Disrupt Cup
• Premier sponsors: Google for Startups and SAP

This year's cohort represents an unusually broad range of sectors. The complete list covers agtech, food, automotive, transportation, biotech, pharmaceuticals, consumer products, cybersecurity, e-commerce, retail, edtech, clean energy, enterprise software, media, entertainment, fintech, health, wellness, human resources, logistics, manufacturing, marketing, real estate, social impact, smart cities, infrastructure, space, and defense.

The complete list of companies

Here are the 2026 Startup Battlefield 200 companies, organized by category. They were chosen from a highly competitive applicant pool, and each will have a presence at Disrupt 2026.

Agtech and food

Canopii; Delight Food Inc.; GreenBox; Krokos Bio; Muju Earth Technologies Limited; Nurtured Nuts Company; Tree Track Intelligence Inc.

Automotive and transportation

Cautio; Laplace Intelligence; Motion Sync Technologies Inc.; MyMonthlyCar Inc.; Neumo; Urbanchain.

Biotech and pharmaceuticals

Alithea Biotechnology; Apertum Neuroscience; BioTrillion; BRAIV; ChemT Biotechnology; Efferon; Infiuss Health; Intero Biosystems; Lume Health Technologies; MyoGene Bio; Nanotrace; OncoRevive; ORAIN Biotechnologies; OrganNexus; Sit Sense Inc.; StuffThatWorks; Sunny.

Consumer

Biuty.ai (BelleTech Inc.); Chaos Audio LLC; Hiyd AI; Hot Girl Hotline; OHLA LLC; Prolo Inc.; Richualist; Swippitt; Tingit; Tressfecta; Yopi SAS.

Cybersecurity

Apate.ai; Cerberus; Cude Labs; DetectifAI Inc.; Hardshell; HODOR; Indora Labs; Magier AI; PsyberCog Labs Inc.; Rein Security; Spektrum Labs; Straiker; Trotta.

E-commerce and retail

Crease Beast; SimplicityDX; Veritas; Wear It AI Inc.

Edtech

BuddyPro; ConfiTalk Ltd (YNova.ai); Efiwe AI Nigeria Limited; Health Allied Licensure; MuseCool; ROYO; ScrollEd; Video Pro Learning; WeGlobal.

Energy and clean tech

Adden Energy; ChargeMate; Climasel Ltd.; CubeNexus; Danu Water; GinnoLab; Helix Carbon; Licube Inc.; LNK Energies; Noble Carbon; Petra Power.

Enterprise tech

AIEEV Inc; Apex Replicant; Ariso; Arusto; Bounty; Buzz & Beyond; Collar AI; Cotriply; Dosu; EasyMate AI; GUDEA; GuideAI; Intelligible; Kilo; Latch AI; Lumai; Marketrix; Mesh; Mirror Particle; Optise; Paraverse Technology Limited; PitchIN; Spiky.ai; StatGenius; Trinitite; YOUnifiedAI Inc.

Entertainment and media

Edit on the Spot; Maka Kids; Press Club; Soundcheck Live.

Fintech

Allin AI; Aparta; CedarDB; DoDocs Inc; Finaive; FirstGlance; Prismm; Tabby; TaxFigure; Zeeh Global.

Government, legal, and regulatory

Bakua AI; Legali AI; Lexsy; Pollsee; Tzun; wellplayd GmbH.

Health and wellness

Care Mojo; Circuit Breaker Labs; Forela; Greenairy Pte Ltd; Imagine Devices Inc.; Lifescapes; LumenUs; MindMuscle; Mutanex; Nervotec; Ocean Friends Inc; OmicXHealth; Predictheon Medical; Provision Surgical; Sama Fertility; TimeTeller GmbH; Uhura Bionics; Valene Health; Virtuosis; Voythos; WoafMeow.

Human resources and recruiting

AskMAE ai LLC; NTangible Inc.; Reel Analytics; SLI.pro; Your360 AI.

Logistics and supply chain

Atreus; Destro AI; Dexa; NuPort Inc; Ridelink Inc; SSINDEX | Stakeholders Sustainability Index.

Manufacturing and industrial

Airs ML; ArgosVision; Bioroom Mycelium Materials; CIT Co. Ltd.; Danu Robotics Ltd; IndustrialNext; MUI Robotics Co. Ltd.; Nexterity Inc.; Niobia AI; Ortex; Planck Lab Inc.; STRUCMEDICA.

Marketing and advertising

Connexa; Floqer; Honeyjar; Intuition Intelligence; LOOQ; onSpark; Prodisphere; Snoika OÜ.

Real estate and proptech

Blueprints AI; Propra Technologies Inc.; Titl; Vitrobot; Watad; Ziffy.ai.

Social impact

Ardook Data; Commissary Club.

Smart cities and infrastructure

ConeLabs; Flox Intelligence; Interlinked; Mosqitter Inc; Snowbotix.

Space and defense

ItsWare Inc; Lola Vision Systems; Longshot Space; Modal Motors Inc.; Public Access LLC; Rocket Fast Drones; SatEnlight; Satlyt; Spacetime Industries; Symphony Space Inc; xOrbita Inc.

A proven launchpad for breakout startups

The Startup Battlefield track record makes this list especially meaningful for early-stage founders. Alumni have gone on to build category-defining companies, including names that are now household brands in cloud storage, web infrastructure, and social communication. Dropbox, Cloudflare, and Discord are often cited as proof that the competition surfaces startups with real commercial potential.

Since its early editions, Startup Battlefield has become known for giving founders a highly visible stage at a critical moment in their growth. The program has a track record of more than $32 billion raised by alumni and more than 250 exits. Those numbers help explain why thousands of founders apply each year, even though only a small fraction can be included.

The 200 startups in this year's cohort cover deep science and frontier technology as well as consumer apps, digital health tools, financial services, climate solutions, and developer platforms. In a typical Startup Battlefield

Source: TechCrunch News

Why Bluetti calls it an e-generator

Bluetti does not want the Pioneer 5000 to be labeled simply as a solar generator, although it is fully capable of being charged by solar panels. The company argues that the term too often suggests a small, limited device that is mainly useful in sunny camping situations. In contrast, the Pioneer 5000 is meant to be a primary electric power source. The “e-generator” branding is intended to position it as a direct electrical replacement for a gasoline or diesel generator, while still embracing its ability to store renewable energy.

The key technical difference is the amount of instant power available. The Pioneer 5000 can deliver up to 18kW of instantaneous surge power. This is important for electrical devices that require an initial burst of energy to start their motors. Tools like air compressors, water pumps, and industrial grinding equipment can easily trip a smaller power station if it cannot deliver high inrush current. By providing this surge capacity, Bluetti wants the Pioneer 5000 to be trusted by contractors and tradespeople who normally carry heavy gas generators to keep their equipment running.

Designed to handle rough conditions

The Pioneer 5000 is not designed to stay inside a climate-controlled garage. It comes with an IP65 rating, which means that it is protected against dust ingress and low-pressure water jets from any direction. This makes it suitable for environments where dust, dirt, rain, or accidental splashes are likely. A construction site, outdoor market, or roadside incident scene can all expose a generator to unpleasant conditions, and Bluetti designed the unit to keep working through them.

The operating temperature range is also broad. Bluetti says the Pioneer 5000 can run in temperatures from -20°C to 55°C, which gives it value in both cold northern climates and hot desert environments. That range is relevant for emergency use, since power failures can happen during freezing winter storms or scorching summer heat waves. The battery system uses lithium iron phosphate chemistry, known in the industry for long cycle life, better heat tolerance, and safer thermal properties than many older lithium-ion cells.

Battery capacity and expansion

The Pioneer 5000 arrives with an internal 5.12kWh battery. That is not enough to run an entire large home for several days by itself, but it is a solid starting point. Bluetti says the system can be expanded to 35kWh with additional batteries. At that level, the company says it can keep an average American home running at full blast for just over a day. In practice, with careful management, it could stretch further because many appliances do not run continuously.

Charging is also flexible. An owner can connect the Pioneer 5000 to AC wall power or to solar input. The unit can combine both sources at once, achieving a charging speed of up to 6.2kW. That means the system can replenish quickly when partial grid power is available, or draw on sunlight during a long-term emergency. A higher charging rate also makes it possible to take advantage of brief gaps in bad weather or opportunities to run a small backup generator.

The case against gas generators

The standard gas-powered generator has several well-known drawbacks. It is noisy enough to disturb neighbors on a job site or in a campsite. A typical open-frame construction generator creates a strong odor of burning fuel, and the exhaust is full of carbon monoxide and particulate pollutants. Gasoline has to be stored, transported, and rotated before it goes stale. Engines require regular maintenance, and they sometimes fail to start after months of sitting idle.

Electric generators have their own limitations, but they solve many of these problems. They are quiet, produce no exhaust, and need no fuel tank. When charged from solar, they produce zero emissions at the point of use. For homeowners who want a cleaner backup power source, an e-generator could mean not having to worry about carbon monoxide indoors, even though all generators should be operated in well-ventilated areas. Bluetti’s product makes it possible to run high-power equipment without the noise and pollution that have historically come with large engines.

But what happens when clouds cover the sky?

There is an elephant in the room for any solar-charged battery generator. It depends on a supply of electricity to be recharged, and the most abundant free supply is sunlight. If a storm knocks out the power grid, the same storm is likely to block solar irradiance for hours or days. Bluetti’s headline advice is practical: customers might still want to keep a small and inexpensive gas generator available to charge the e-generator when solar power is not enough.

This is not a failing of the Pioneer 5000 specifically; it is a reality of battery-based electric generators. They are energy storage devices rather than energy sources.

Source: The Verge News