Anthropic is testing a new personal finance feature called "Claude Money" that will allow you to connect your bank accounts directly to Claude and "understand your money." [...]
AirPods 5 bring Active Noise Cancellation to Apple’s most affordable current AirPods while pushing the open-ear design closer to capabilities previously associated with more expensive models. The new generation starts at $129 and uses redesigned acoustics and computational audio to remove up to 50% more external noise than AirPods 4 with Active Noise Cancellation.
Apple is also splitting the range into two versions. The standard $129 AirPods 5 include ANC and USB-C charging, while a $149 model adds a Wireless Charging Case, longer battery life, a case speaker for Find My, and volume adjustment directly from the stems.
Both models are available for preorder ahead of their September 18 release in the U.S. and more than 65 other countries and regions.
The changes make the distinction between Apple’s entry-level AirPods and its more advanced headphones less dependent on headline software features. Adaptive Audio, Transparency, Conversation Awareness, Personalized Spatial Audio, Live Translation, and Siri AI are all part of the AirPods 5 experience.
Image Credit: Apple Inc.
AirPods 5 push open-ear ANC further
Active Noise Cancellation is particularly difficult to implement in an open-ear earbud because there is no silicone tip forming a physical seal inside the ear canal.
Apple has approached that limitation with a new multiport acoustic architecture, revised computational audio algorithms, and the H2 headphone chip. The company says AirPods 5 can cancel up to 50% more noise than AirPods 4 with ANC.
That improvement does not turn the design into an AirPods Pro-style sealed earbud. AirPods 5 remain open-ear headphones, retaining the fit preferred by people who dislike the pressure or isolation associated with in-ear tips.
The acoustic system has also been redesigned around a wider variety of ear shapes. Next-generation Adaptive EQ continuously adjusts sound based on how each earbud sits, while Apple says the revised architecture produces richer and more detailed playback.
Transparency mode has been updated to reproduce voices and environmental sounds more naturally. Adaptive Audio can move between Transparency and noise cancellation according to surrounding conditions, while Conversation Awareness automatically reduces media volume when the wearer begins speaking to someone nearby.
Personalized Spatial Audio with dynamic head tracking remains supported for compatible music, movies, and other content.
AirPods 5 are rated IP57 for dust, sweat, and water resistance, making them better suited to workouts and changing weather conditions, although they are not waterproof.
Two AirPods 5 models create a meaningful $20 decision
Apple’s pricing makes the $149 Wireless Charging Case version more than a different charging option.
Standard AirPods 5 provide up to four hours of listening on one charge with ANC enabled and up to 20 hours when the USB-C case is included.
AirPods 5 with Wireless Charging Case increase those figures to five hours from the earbuds and up to 22 hours with the case while ANC is active.
The upgraded case can charge through USB-C, an Apple Watch charger, or a Qi-certified wireless charger. It also contains a speaker that can assist with locating the case through Find My.
The more expensive model introduces another difference directly on the earbuds. Users can swipe up or down on a stem to adjust volume, bringing a control familiar from AirPods Pro to Apple’s open-ear form factor.
That leaves buyers with a relatively straightforward tradeoff. The $129 version provides the central audio experience, including ANC, without requiring a step up for the main listening features. Spending another $20 adds more convenient charging, slightly longer battery life, Find My assistance from the case speaker, and physical volume adjustment.
Placing either AirPods 5 model in its charging case for five minutes provides about one additional hour of listening.
Image Credit: Apple Inc.
Siri AI and Live Translation extend the earbuds beyond music
AirPods 5 arrive as Apple makes voice interaction a larger part of the AirPods experience.
When paired with a compatible iPhone, Siri AI can be accessed hands-free. The assistant can work with personal context from information such as messages, emails, and photos, respond to open-ended questions, and perform supported actions in apps.
Head gestures remain part of the experience. Users can nod to accept certain Siri prompts or shake their head to decline them, allowing calls and notifications to be handled without speaking or touching the earbuds.
Live Translation provides another use for the microphones and ANC system.
After compatible languages are downloaded and the feature is configured through Apple’s Translate app, a user can hear another person’s speech translated through AirPods. Translation can be started through Siri, the iPhone Action button, the Translate app, or by pressing and holding both AirPods stems simultaneously.
When two people are each using compatible AirPods and supported iPhones, both participants can hear translated versions of the conversation in their chosen languages.
Live Translation requires an Apple Intelligence-enabled iPhone 15 Pro or later, compatible AirPods, current software, and the latest AirPods firmware, with availability varying by language and region. Apple also notes that generated translations can contain errors, making verification appropriate when accuracy is especially significant.
AirPods 5 narrow the gap within the lineup
The fifth-generation model represents a different kind of upgrade from simply adding another listening mode.
ANC is now standard across both AirPods 5 configurations, instead of requiring buyers to choose a separate ANC version as they did with AirPods 4. Apple has then moved the pricing distinction toward charging, battery endurance, Find My hardware, and physical controls.
The earbuds continue to use Bluetooth 5.3 and the H2 chip, with dual beamforming microphones, an inward-facing microphone, optical in-ear detection, motion and speech accelerometers, and force sensors built into the stems.
Each earbud weighs 4.3 grams, preserving the lightweight character of the open-ear line despite the additional capabilities.
The new acoustic architecture may be the more consequential change over time. Open-ear earbuds cannot rely on a tight seal to block surrounding sound, so gains in ANC depend heavily on microphone placement, acoustic design, fit, and real-time processing. Apple’s claim of up to 50% more noise removal over the previous generation represents a substantial step for that format rather than simply matching the behavior of sealed earbuds.
AirPods 5 start at $129, while AirPods 5 with Wireless Charging Case cost $149. Both reach stores on September 18, giving the open-ear AirPods line a standard feature set that now includes noise cancellation without pushing buyers to a higher-priced model to get it.
The company also disclosed previously unreported incidents in which its AI models behaved in misaligned ways, including uploading files to the internet without being asked.
The company also disclosed previously unreported incidents in which its AI models behaved in misaligned ways, including uploading files to the internet without being asked.
The first beta of iOS 27.2 includes Apple's redesigned Health app, giving developers a chance to try out the features ahead of launch later this year.
Apple's developer notes for the feature say the Health app is being tested in this beta, but it won't be in the launch version of iOS 27.2. It will instead come later in the year.
The Health app will incorporate Apple Intelligence to turn data and metrics collected by the Apple Watch into personalized guidance. Here are the new features we can expect:
Insights - The Insights tab offers an Apple Intelligence-generated summary of timely information like your latest sleep metrics or a new peak in VO2 max. There are options to get custom advice on readiness, fitness, heart health, and more in the For You section.
Personal Guidance - Personal guidance is part of the Insights tab. It knows what's important to you, and will make relevant suggestions. Apple says if you're working on your cardio, you might see a suggestion to incorporate intervals into your morning run. Recommendations adapt to each user and their data.
Readiness - The new Apple Watch models calculate a readiness score that takes into account recent activity, vitals, and sleep score. It gives a 0–10 score with recommendations on activity level. Readouts include Recover, Pace Yourself, Ready, or Go For It. Info is shown on the watch, in the Fitness app, and in the Health app's Insights tab.
Fitness - Insights incorporates your daily activity and it updates throughout the day.
Vitals - The Vitals option in the Health app has new overnight views and daytime views. HRV is incorporated as a metric, offering a better look at your ability to recover from stress. It measures overall HRV and recovery HRV.
Longevity - There is a new Longevity tab in the Health app. It uses metrics from your iPhone and Apple Watch to assess overall health across heart health, sleep, mental wellbeing, movement health, metabolic health, hearing, and nutrition. The section shows you how well you're doing in each category, and how each metric is classified against the latest clinical guidance.
Health Age - Health Age is part of the Longevity tab. It uses metrics like VO2 max, resting heart rate, HRV, sleep, and biomarkers from blood work to determine how your health metrics compare to your actual age. It will help you figure out what habits make the biggest impact on your health, and how you can improve.
At-home health assessments like a VO2 max test will be coming to the Health app, taking advantage of the iPhone's camera to track your movements as you follow instructions from a trainer. There will be in-depth physical assessments for measuring flexibility, strength, balance, and movement mechanics in addition to VO2 max.
Apple also plans to eventually offer U.S. users the option to buy a bloodwork panel from Quest that covers more than 50 biomarkers. The test is priced at $119, and users will be able to order it right in the Health app.
Not all of the new features are going to be available in the beta. CNET says ordering labs, personalized health info in the Insights tab, and personalized recommendations will not be available until later in the year. Features are going to roll out progressively.
The Spanish Data Protection Agency (AEPD) was notified of an attack allegedly carried out with an AI agent powered by a known large language model (LLM). [...]
Our solution hinges on splitting the Apple Reference Image process into two phases: creating a secure digital negative, and developing that negative into a reference image. Each phase receives our strongest protections.
[…]
Apple Reference Image provides both a lower bound and an upper bound on capture time from Apple’s cryptographic timestamp service, and we guarantee the photo was taken between the two bounds. On a regular heartbeat, the device requests a cryptographic timestamp token, and retains the most recent one it has received. Globally this happens on average every 15 minutes, though the interval depends on local network conditions. This provides a proven lower bound timestamp for the photographic capture. After capture, the device requests a second timestamp to use as an upper bound, and both timestamps are embedded and signed with the sensor data.
[…]
When the user chooses to create a reference image, the device uploads the digital negative to PCC, which runs the processing steps needed to render the image — including demosaicing, tone mapping, and compression — in a highly secure, private, and verifiable environment. Experts can verify that PCC doesn’t alter a digital negative during development: they can examine the software that does the work.
[…]
Finally, as no security system is perfect, we created a revocation system that can revoke individual photos, as well as all photos from a specific sensor.
[…]
We built Apple Reference Image to avoid using an explicit, public credential for photographers, and to avoid even implicit public association between different photos taken by the same sensor. The final reference image is instead signed by Apple’s signing service, after validation by PCC.
A growing divide among leading AI companies over how to secure increasingly powerful models is beginning to translate into challenges for enterprise IT, with implications for how organizations access, deploy, and govern AI systems.
The latest flashpoint came after Meta CEO Mark Zuckerberg called for neutral evaluators to independently test AI models, pushing back on calls from rivals to slow development or tighten coordination.
“Engaging independent evaluators and advisors is industry best practice,” he added, noting that Meta already does this in several areas.
His comments follow a series of public proposals from AI industry leaders including Dario Amodei, who argued for a more cautious pace of development, and Sam Altman, who called for collaboration on safety standards.
The debate has intensified amid disclosures from AI labs and policymakers on potential misuse of advanced systems. Anthropic has said it restricted attempts to use its Claude models in sensitive domains, while OpenAI has engaged with policymakers on AI-related risks, according to company statements and reports.
Enterprise concerns
While the debate is often framed as a choice between slowing innovation and strengthening oversight, analysts said enterprises should focus less on which approach prevails and more on the operational consequences already taking shape.
“Divergent safety approaches will make access to advanced AI models less predictable, rather than producing an industrywide slowdown,” said Sushovan Mukhopadhyay, director analyst at Gartner. Vendors are likely to apply different release schedules, regional availability, access tiers, and usage restrictions, he said, meaning enterprises could encounter similar capabilities “at different times and under materially different conditions.”
Mukhopadhyay said enterprises should plan for variability in access rather than assuming consistent availability across providers or geographies.
“I read this week as the point where frontier AI became a managed supply,” said Bhupendra Chopra, chief revenue officer at Kanerika. “For three years CIOs could assume the next model would simply show up. A frontier model now behaves more like a critical component from a supplier whose delivery dates depend partly on outside reviewers and export rules.”
Chopra added that “any AI roadmap built on a specific model arriving on a specific date is carrying supply risk it hasn’t priced.”
Security pressure builds regardless of slowdown
Analysts said slowing development alone is unlikely to materially change enterprise risk, particularly as open-source models proliferate.
“The biggest point isn’t the pause itself. It’s that the leaders of AI companies are agreeing on something,” said Nikhil Gupta, founder and CEO of ArmorCode.
Gupta said the threat landscape has already shifted. “Even if companies hit pause, open-source AI models are already out there,” he said. “I’m not convinced slowing down some companies meaningfully changes what adversaries can do.”
“Even if AI development slows down tomorrow, security must accelerate,” Gupta added. “The job of securing these systems has effectively gotten ten times harder.”
A new ‘AI assurance’ layer emerges
The focus on evaluation is driving what analysts described as an emerging “AI assurance” layer, where third parties assess models for safety and compliance.
“A distinct AI assurance layer is likely to emerge, but enterprises should not expect a single certification to establish that an AI system is safe,” Mukhopadhyay said. “Enterprise risk also depends on data, system instructions, tools, agents and deployment controls.”
Chopra said enterprises risk misinterpreting such evaluations. “Procurement teams may see a third-party evaluation and treat the model as vetted,” he said. “Within a year it becomes a checkbox.”
Instead, he said, enterprises will need to run their own validation. “CIOs who get ahead will test each model against their own data before it touches production.”
Fragmentation complicates multi-model strategies
For CIOs pursuing multi-vendor strategies, differing approaches across providers could introduce additional complexity.
“Fragmentation was already the default. Safety divergence deepens it,” Chopra said.
He said risk is most acute during transitions. “For an enterprise running several models, the exposure sits in the handoff,” he said. “When a model is delayed or replaced, the system can behave differently.”
“I’d rank untested model substitution above vendor lock-in,” Chopra said.
Gupta said open architectures will be important. “The framework needs to be open, not locked to any single vendor,” he said.
Mukhopadhyay added that enterprises should prepare for models becoming unavailable or restricted.
CIOs urged to build resilience
Analysts said enterprises will need to design AI strategies that can adapt to changes in availability, pricing, and governance.
“For critical applications, CIOs should separate application controls and business logic from the underlying model,” Mukhopadhyay said.
Chopra emphasized flexibility. “A routing layer between applications and model providers turns switching into configuration work,” he said, adding that contracts should cover deprecation timelines.
He also pointed to pricing implications. “Scarce access to the frontier starts to carry a premium,” Chopra said.
AWS is betting that AI agents need a different interface as they move beyond answering prompts and start working autonomously in the background.
The company has open-sourced Pizza Bot, a self-hosted application that gives users an inbox for managing work delegated to AI agents, with separate threads for ongoing tasks and a queue for work that is completed or needs human input, rather than keeping the management of agents restricted inside a conventional chat window.
The rationale, according to AWS, is that background agents do not always need a user’s attention while they work, and an inbox model will let users hand off longer-running tasks, return to them later, and see which jobs are complete or require intervention.
Under the hood
That approach is reflected in how the inbox organizes work with the help of an “All” tab that contains the history of each task or conversation, including the agent’s messages and work performed, the “Unread” tab that flags completed work that users have yet to review, and an “Action” tab that surfaces tasks paused while waiting for user input or approval.
The inbox interface also has a panel named Activity that shows users how an agent handled a particular task along with the transcript, AWS wrote in a blog post introducing Pizza Bot.
AWS’ inbox-oriented rationale also extends to Pizza Bot’s architecture.
It uses LangChain’s Deep Agents as the harness and LangGraph as the stateful runtime, with a combination of the two allowing an agent to checkpoint its progress as it works, preserving its messages, tool activity and current state so a task can be paused and resumed rather than being tied to a live chat session, the hyperscaler wrote.
Pizza Bot’s server sits on top of that stack, connecting the agent runtime with the user interface, skills, MCP servers and the model provider, with developers able to choose from Anthropic, OpenAI, Google Gemini and Amazon Bedrock, as well as local models through Ollama, it added.
As for its out-of-the-box capabilities, the application comes with skills for working with files, browsing the web and delegating tasks to specialist agents. Developers can also add existing Agent Skills and MCP servers to give the agents access to other tools and services, the hyperscaler wrote.
Enterprise integration could remain a hurdle
However, analysts were not too convinced that Pizza Bot’s out-of-the-box skills and support for existing tools will make its implementation easier for enterprises.
While the out-of-the-box capabilities and added support for existing skills saves enterprise teams the time to build the application, it leaves out the integration work required to make it work, said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.
“Integration is where most of the money in an enterprise agent deployment goes. A sales or finance team gets value from an agent when it can read and update CRM, email and ERP, and each of those systems needs a connector someone has to build, secure and maintain,” Chopra pointed out.
Pizza Bot’s integration could be further complicated by the application’s lack of support or any SLA, according to Manoj Chandra Jha, principal analyst at Nord-IQ Research. That means the integration and operational burden ultimately falls on enterprises, who would be responsible for running, securing and maintaining the open source software themselves, Jha said.
Rise in enterprise productivity likely
But for enterprises willing to take on that integration work, the inbox-oriented approach could improve productivity.
“It’s a meaningful shift because it changes the economics of delegating work to agents. A chat interface requires a person’s attention throughout the task, while an inbox brings them in only when their judgment is needed, much like how executives delegate work to their teams,” Chopra said.
“Coding agents have already demonstrated this model, where an engineer assigns an issue and reviews the resulting pull request. Pizza Bot extends that approach to tasks such as meeting preparation and follow-ups,” Chopra added.
Further, the analyst pointed out that the inbox-oriented approach could give enterprise teams a clearer window into how scheduled tasks perform.
“For scheduled tasks, having the results delivered as threads gives users a way to track what happened in the background and spot failures that might otherwise go unnoticed,” Chopra said.
Out of sight, out of mind
There are risks with this approach, however: “The inbox model can make bad work less visible. When somebody is watching an agent in a chat window, they can see it going off the rails,” said Phil Fersht, CEO of HFS Research.
In contrast, when hundreds of tasks are running quietly in the background, users may not see an agent making mistakes until the task is complete or an exception requires their attention, potentially making problems harder to catch early, Fersht said.
That reduced visibility can also create approval fatigue, according to Chopra.
“An agent sending back dozens of threads with multiple approval requests can condition users to approve them without reading them closely,” Chopra said.
There is also a risk in the delay between an agent preparing an action and a user approving it: “Information that was current when the agent paused may no longer be valid hours later, potentially resulting in an outdated CRM update or a meeting invite for a slot that is no longer available,” he said. “An assumption that might be caught in a live chat can go unchallenged as the agent continues building on it and consuming model resources. Scheduled runs can also add costs without anyone watching them in real time.”
Those disadvantages can be countered by designing processes with fewer, better-timed approval points, and verifying that the underlying data is still current before allowing an action to execute, he said.
Adoption likely to start with technical teams
Such tradeoffs are likely to shape where Pizza Bot gains traction.
“Adoption will likely be bottom-up, with individual technologists and small platform teams drawn to Pizza Bot’s control and standards-based design,” Jha said. “Risk-averse industries, especially ones in the regulated sectors, and business users with limited technical expertise, however, are likely to approach the application more cautiously,” he added.
That could leave Pizza Bot occupying a more targeted role in the enterprise, as a way for technical teams to experiment with asynchronous agents and new ways of delegating work, rather than an immediate replacement for the governed interfaces and managed services enterprises typically use for business-critical processes, he said.
In iOS 27, available now, Apple has made some changes and additions to the iPhone's stock Camera app, including the addition of a new Siri mode.
Available in addition to the Video, Photo, and other camera modes, Siri mode can be accessed by swiping across the mode carousel at the bottom of the screen.
The new mode uses Visual Intelligence to identify objects in the frame and provide information about them. It can be used to identify plants, animals, food, and more. If it's food you want info on, for example, it can tell you what it is, and how many calories are in it.
Other examples Siri mode can be used for include capturing a photo of an event flyer and adding a date to the Calendar app, text translation, splitting a bill, and more.
In Siri mode, a button on the right lets you search Google Images, and a button on the left lets you ask a specific question. So you can ask for help to cook the food in the frame, for instance.
Apple has also tweaked the way the camera interface works in other ways. Quick access tools for activating flash, Live Photo, and Night mode are now located at the top-center of the UI, while the full set of tools can be accessed from the bottom-right instead of the top-right. It's also possible to customize which controls are displayed at the top of the screen.
Apple made iOS 27 available to download on supported iPhone models on September 14, a few days ahead of the launch of new iPhone 18 Pro models on September 18.
According to new reporting by The Guardian, nearly all of the top ten global producers of forever chemicals, otherwise known as polyfluoroalkyl substances, or PFAS, plan to ramp up production quotas on the back of the data center boom.
There’s a clear market demand for this: PFAS play a huge role in data center construction, where they’re used in cooling systems, AI chips, circuit boards, and fire suppression systems — not to mention the coating on thousands of miles of cables used to connect these components together.
Though components laden with PFAS are found throughout the industrial world, their use in data centers is particularly troubling. Because these facilities run almost non-stop save for the rare pause for maintenance, their components break down remarkably quickly, which means data center owners are constantly scrapping old parts in favor of new ones.
When those old components are tossed to the curb, the PFAS will almost inevitably wind up in the environment, increasing the likelihood that they wind up in the food chain. When we eat contaminated plant and animal products, these forever chemicals accumulate in our body, where they may contribute to a laundry list of debilitating health problems.
“These companies’ expansion plans show that unless governments and regulators insist on a rapid phaseout, we will face a new tidal wave of forever chemicals,” Anne-Sofie Bäckar, executive director of Swedish chemical watchdog ChemSec, told The Guardian.
Of course, PFAS are far from the only toxic discharge attributable to the AI data center boom. On Monday, the Gothamist reported that nearly 5,000 gallons of diesel fuel had somehow spilled out of the Equinix Data Center in Secaucus, New Jersey, flowing directly into a tributary creek in the New Jersey Meadowlands.
As Climate Revolution Action Network executive director Ben Dziobek told the Gothamist, it’s proof that data centers harms extend far beyond their property lines.
“Leaving this [data centers] to individual towns doesn’t make any sense,” Dziobek said. “Data centers create problems that don’t stop at municipal boundaries, whether it’s pollution or straining the grid and driving up everyone’s electric bills.”
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