Apple’s “Surprise and Shine” event is days away, and there are plenty of rumors swirling about new devices we can expect, like a new Apple Watch and the first foldable iPhone, which could be called the iPhone Ultra. 

When we asked CNET readers one thing they’d change about the iPhone in our Big Guessing Game, 16% said they want it to cost less. But that’s not happening. In fact, you should expect new models to cost significantly more.  

TrendForce, a global market research firm, released a report on Thursday suggesting you can expect higher prices instead of steep discounts. The firm expects the iPhone 18 series to cost 10 to 20% more due to the ongoing RAM shortage and higher hardware costs. 

“For the 256GB Pro model, memory costs in [the third quarter of 2026] are expected to be nearly 400% higher than a year earlier,” TrendForce said. “Despite Apple’s efforts to negotiate lower prices for other components, these savings are unlikely to offset the resulting pressure on overall [materials] costs.”

Apple raised prices on select products in June due to the RAM shortage, but excluded iPhones from the price hikes. Now there’s a chance that Apple could release some iPhone 18 models with higher prices at the September event. TrendForce said it expects Apple to absorb some of the costs to keep prices for the new models as part of a “relatively moderate” pricing strategy. 

So exactly how much more are we talking about? Some analysts believe the foldable iPhone Ultra could cost between $2,000 and $2,500. TrendForce predicts the starting price will be slightly higher — between $2,099 and $2,299. The phone could go up to $3,000. TrendForce didn’t immediately respond to a request for further comment. 

But higher costs aren’t equivalent to big improvements. 

TrendForce says we shouldn’t expect major performance upgrades from Apple’s upcoming iPhone lineup. Instead, we should expect a more powerful processor for AI features and power efficiency, better chip cooling technology and power-efficient displays. And the iPhone 18 Pro models’ main camera could have a “mechanical variable aperture” to improve depth-of-field control and dynamic range for different lighting settings. 

The really big upgrade is the potential release of the folding iPhone Ultra and bigger batteries for premium iPhone 18 models, TrendForce predicts. The iPhone 18 Pro Max could see the biggest upgrade due to a larger chassis. CNET’s Patrick Holland tested smartphone batteries last year and found that the iPhone 17 Pro Max was the best for battery life, so iPhone 18’s premium models could be an even bigger upgrade from the current model. 

Dashia Milden

Senior Consumer Insights Editor

Dashia is the consumer insights editor for CNET. She specializes in data-driven analysis and news at the intersection of tech, personal finance and consumer sentiment. Dashia investigates economic shifts and everyday challenges to help readers make well-informed decisions, and she covers a range of topics, including technology, security, energy and money. Dashia graduated from the University of South Carolina with a bachelor’s degree in journalism. She loves baking, teaching spinning and spending time with her family.

See full bio

Advertisement

First look: Range Rover has introduced its first all-electric model, putting an 800-volt battery system and dual-motor drivetrain into its flagship SUV. It marks a major technical step for Jaguar Land Rover, combining fast-charging hardware, a large battery pack, and software-controlled off-road systems in a vehicle designed to preserve the Range Rover’s established identity.

The 2027 Range Rover Electric will be built at Jaguar Land Rover’s Solihull plant in England. It uses a 118.5-kWh battery pack, two electric motors, and a claimed EPA range of at least 333 miles. JLR says the SUV can charge at up to 350 kW, adding about 125 miles of range in 10 minutes. A 10% to 80% charge is expected to take 22 minutes.

The vehicle arrives about a year later than originally planned. It also comes as JLR works through supply-chain issues, lower profits, a restructuring program, and the aftermath of a cyberattack that halted production for more than a month last year.

JLR has upgraded its manufacturing operations in the West Midlands for EV production. The company said it has trained roughly 9,000 employees at Solihull for electrification work, along with another 1,500 workers elsewhere in the region. Its Electric Propulsion Manufacturing Centre in Wolverhampton is producing battery packs and electric drive units alongside internal-combustion engines.

The Range Rover Electric is based on JLR’s MLA platform, which was designed to support combustion, hybrid, and battery-electric powertrains. The electric version largely retains the shape of the current Range Rover. It has a revised grille, aero-focused wheels, and a flat underbody, but otherwise looks much like the gasoline and plug-in hybrid models.

Its front and rear motors are identical permanent-magnet synchronous units, each rated at 349 horsepower. Together, they produce 542 horsepower and 627 pound-feet of torque. JLR said the motors are 24% more efficient than those used in the Jaguar I-Pace.

The powertrain uses silicon-carbide inverters that can switch in less than a millisecond. Each motor contains 144 copper hairpin windings, with 0.2-millimeter laminations and a 0.7-millimeter gap between the rotor and stator. The power electronics are mounted in the space normally occupied by the transmission tunnel.

AESC supplies the battery cells, which use nickel-manganese-cobalt chemistry. The pack contains 10 modules arranged in a double-stack configuration. JLR said aerogel spacers help manage heat between the cells. Its ThermAssist system recovers waste heat from the powertrain. The company said the system can increase range by 7% and reduce cabin-heating energy use by 40%.

US-market vehicles will include a NACS charging port, Tesla Supercharger access, and ISO 15118 plug-and-charge capability. The SUV can also provide up to 3.6 kW of AC electricity through vehicle-to-load capability. JLR plans to add vehicle-to-grid support later.

The battery adds weight and also changes the SUV’s handling. The electric Range Rover is only 165 pounds heavier than the V8 version because it eliminates components including the front, center, and rear differentials and their driveshafts. The battery lowers the center of gravity by 3.4 inches, while its aluminum frame increases chassis stiffness by 56%, according to JLR.

Range Rover expects the electric SUV to reach 60 mph in 4.3 seconds. It has air suspension, twin-valve dampers, and rear-wheel steering that turns the rear wheels by up to 7.3 degrees. The vehicle weighs 6,195 pounds. One-pedal driving is optional, with regenerative braking of up to 0.2 G.

JLR says the electric model is 7 decibels quieter than the V8. Engineers added isolation around the front drive unit and addressed noise transmitted into the cabin through high-voltage cables. An active noise-cancellation system uses thin membrane speakers developed with Warwick Audio and installed in the headrests.

The electric Range Rover is designed to retain much of the off-road capability of its combustion-powered counterpart. Ground clearance is up to 10.3 inches, slightly less than the V8 model because of the rear drive unit. It can wade through up to 36 inches of water.

Instead of mechanical locking differentials, the electric model uses software-based torque management to control traction at the front and rear axles. JLR said the system can adjust power delivery based on available grip.

JLR said 80,000 people have registered their interest in the vehicle. About half are in North America, and 70% are new to the brand.

The rumors were true, all of them (and then some): OpenAI today is releasing GPT-6 Astra, a new frontier model that the company says likely marks the onset of artificial generalized intelligence (AGI), its long sought goal of “highly autonomous systems that outperform humans at most economically valuable work.”

In a closed a press briefing earlier today, OpenAI co-founder and president Greg Brockman offered an unusually direct formulation of that message, ending the session with: “Welcome to the AGI era.”

That is an unusually consequential framing even by the standards of frontier AI launches. But for enterprises, the more immediate significance of Astra may be considerably more concrete: OpenAI is positioning GPT-6 Astra as a new era of computing in which users, including employees, no longer have to click around a mouse or type on a keyboard ever again (if they don’t want).

OpenAI’s launch materials, provided in advance to VentureBeat, call it “the world’s best computer use model.”

Instead of requiring developers to build a dedicated API integration for every application an AI system needs to use, Astra is designed to navigate software much as a person does — working across browsers, spreadsheets, websites and desktop applications, producing finished documents and presentations, and carrying out multistep workflows rather than merely telling a user how to complete them.

Indeed, the company showed off a promotional video for GPT-6 Astra that began with a 1980s AI demo of a person asking a computer to draw a yellow circle, which it did simply, before cutting to today and showing various OpenAI employees interacting with Astra through voice, asking it turn a yellow circle into a rocket ship and then a full 3D game in minutes, and create a listing on eBay, all from voice input alone.

Astra begins rolling out Thursday to enterprise customers with OpenAI’s gated access program, Daybreak. OpenAI says it will become available over the coming days to ChatGPT Plus, Pro, Business and Enterprise customers, as well as through the OpenAI API and cloud platforms including AWS Bedrock and Microsoft Azure.

From answering questions to operating computers

The enterprise case for Astra rests heavily on computer use.

OpenAI says the model can fill out online forms, update CRM records, organize calendars, conduct web research and draft results into documents or email. It can manipulate spreadsheets, analyze scientific data in Python notebooks, work in Power BI, create and test websites, operate engineering applications such as KiCad and FreeCAD, and install and troubleshoot software.

Those capabilities point toward a potentially important change in enterprise AI architecture.

For much of the generative AI boom, companies have needed to connect models to corporate systems through APIs, plugins, retrieval systems and purpose-built tools. Brockman argued that computer-use agents could begin bypassing some of that integration work because software already exposes an interface designed for a highly general-purpose intelligence: the human user.

“We’ve been bottlenecked over this gigantic era by people writing connectors and very painstakingly building these connections into all these tools that people can already use,” Brockman said.

With sufficiently capable computer use, he added, an agent can instead “zip through spreadsheets, fill out forms, [and] navigate across web pages.”

The idea goes back to OpenAI’s earliest days, Brockman said, when researchers discussed training an agent around the same basic inputs and outputs available to humans using computers: pixels, keyboards and mice.

“I feel like we’ve really achieved the first agent that feels like it’s actually able to do that in a way that’s just so extremely useful,” he said.

OpenAI reports that on an offline subset of OSWorld 2.0, Astra scored 72.6% while taking roughly 40 minutes per task, compared with GPT-5.6 Sol’s 65.7% at roughly 75 minutes — approximately 47% less time per task.

The company also demonstrated Astra performing tasks ranging from creating a 3D game to preparing a legal agreement while simultaneously handling unrelated requests. The broader message was that the model is intended to move beyond the familiar chatbot pattern in which humans continually provide the next instruction.

“With Astra, users have incredible capabilities at their fingertips and can do things that seemed very far away less than a year ago,” OpenAI researcher Mia Glaese said during the briefing. “With those capabilities, we expect people to delegate much more complex work across applications, with humans directing the work at a much higher level.”

That shift — from prompting AI to supervising AI — may ultimately matter more to businesses than another increase on an academic benchmark.

OpenAI says Astra represents its biggest training jump yet

Aidan Clark, an OpenAI researcher who discussed Astra’s development during the briefing, described it as the company’s largest-scale training run.

According to Clark, Astra is the first OpenAI model pretrained using more than 100,000 DBUs at the company’s Stargate infrastructure and the first for which previous models played a major role supervising the training of the next model.

“Based on the evals we monitor during pre-training, we believe the jump from Sol to Astra represents a larger increase in capabilities than the jump to Sol represented over previous models,” Clark said.

OpenAI attributes Astra’s capabilities to the combination of large-scale pretraining and reinforcement learning intended to teach the model to connect information and execute increasingly long tasks.

The resulting benchmark numbers are striking.

OpenAI reports Astra scores 97.6% on FrontierMath Tier 4 v2, 74.1% on DeepSWE v1.1, 95.9% on BenchCAD, 96% on GPQA Diamond and 100% on ExploitBench. It also reports a 98.6% score on ARC-AGI-3.

But that last number comes with an important qualification — and highlights a growing problem with how the industry talks about model intelligence.

If Astra scores 98.6% on ARC-AGI-3, is that AGI?

ARC-AGI has become one of the most closely watched attempts to measure whether AI systems can generalize to unfamiliar problems rather than reproduce capabilities acquired through training.

On the current ARC-AGI-3 leaderboard, conventional frontier-model runs sit dramatically below Astra’s reported 98.6% result.

But the comparison isn’t straightforward.

OpenAI’s own evaluation notes say Astra uses the company’s Responses API harness, while comparison models can operate under different configurations.

That distinction matters because another recent ARC-AGI-3 result demonstrated just how much performance can come from the system surrounding a model.

In August, NVIDIA reported that its Agentic Variation Operators, or AVO, architecture achieved a 100% score across all 25 environments and 183 levels in the ARC-AGI-3 public set. But NVIDIA did not create a foundation model that suddenly jumped to 100%. AVO used Claude Opus 5, and NVIDIA said the underlying model’s baseline was roughly 30%.

AVO adds mechanisms including persistent memory, tools, feedback and recovery, allowing an agent to maintain progress over long-running tasks rather than treating every interaction as effectively isolated.

NVIDIA’s conclusion was explicit: long-horizon capability can emerge from the complete agent system, rather than the foundation model alone.

That debate has already spilled into the AI community. One r/singularity user argued that ARC-AGI-3’s restrictions on retaining context across actions made the benchmark an unrealistic representation of how production agents operate, comparing it to testing humans while repeatedly erasing what they had learned.

Other commenters have pushed in the opposite direction, arguing that adding elaborate harnesses makes it harder to determine whether the underlying model has actually generalized. One commenter responding to NVIDIA’s result wrote: “Let’s see if the capabilities generalise or if it was just overtrained on this specific benchmark.”

The disagreement exposes an increasingly important question for claims about AGI: What exactly is the object being measured?

A foundation model? A model plus persistent memory? A model with a computer, browser and tools? Or the complete deployed system?

For enterprises, the distinction may eventually become less important operationally. Companies buy outcomes from systems, not benchmark purity. If an agent can reliably reconcile accounts, investigate an incident, modify a production codebase or assemble a financial model, whether that ability originates primarily in neural weights, memory architecture or tool orchestration may matter less than its cost, reliability and auditability.

And OpenAI appears increasingly willing to make that argument.

“Everyone has a different definition of AGI,” Brockman said. “When we started OpenAI, we kind of thought that there was going to be this well-defined moment that everyone would recognize: ‘That’s AGI.’ It hasn’t played out like that. It’s a much more gray, fuzzy thing.”

But Brockman went considerably further when asked whether Astra itself qualifies.

“For me personally, I do think we’re there,” he said. “I think there’s a pretty good argument for it.”

Later, he offered perhaps the clearest formulation of OpenAI’s position: “I think it’s not unreasonable to feel that we are now in the AGI era.”

No GDPval?

One notable omission from OpenAI’s Astra launch materials is GDPval, the company’s own benchmark for measuring performance on economically valuable, real-world work. OpenAI introduced GDPval in 2025 specifically to move beyond academic-style tests and coding benchmarks, evaluating models on 1,320 tasks drawn from 44 knowledge-work occupations across nine major U.S. industries. Those tasks include deliverables such as legal briefs, engineering designs, spreadsheets, presentations, customer-support work and nursing care plans — much closer to the enterprise workflows OpenAI now says Astra is designed to automate.

That makes the absence conspicuous given the AGI framing around Astra. OpenAI originally positioned GDPval as a way to ground discussion about AGI and economic impact in observable workplace performance rather than speculation. Its own description says the benchmark was created to track how well AI systems perform on “economically valuable, real-world tasks” and to provide a clearer picture of how models might support professionals in everyday work. In other words, if Astra’s significance is that enterprises can now delegate materially more work to AI, GDPval would appear to be one of OpenAI’s most directly relevant internal yardsticks for substantiating that claim.

The omission does not invalidate Astra’s other results, but it does leave an analytical gap. OpenAI’s 98.6% ARC-AGI-3 score speaks to interactive reasoning and adaptation, while benchmarks such as DeepSWE and Agents’ Last Exam capture specific forms of software engineering and professional workflow performance. GDPval, by contrast, was explicitly designed to ask a broader economic question: can models produce work products comparable to those of experienced professionals across a wide cross-section of occupations? OpenAI’s earlier results showed frontier systems approaching expert-level quality on some of those tasks, with substantial gains from GPT-4o to GPT-5.

There is also an important limitation in GDPval that may help explain why OpenAI did not center it here. The current version is one-shot: it does not measure the long-horizon, interactive, multi-application work that Astra is supposed to excel at. OpenAI itself has said future versions should add iterative workflows, richer context and ambiguity. That means GDPval is arguably both highly relevant to Astra’s enterprise story and somewhat mismatched to its most agentic capabilities.

Still, given Brockman’s “AGI era” framing, the missing number is worth noting. If the practical case for AGI is increasingly about whether AI can perform economically meaningful work across many professions, then GDPval is one of OpenAI’s clearest attempts to measure exactly that. Until Astra results appear there — or on a successor designed for multi-step agentic work — claims about its broad economic generality rest more on a mosaic of specialized benchmarks and demonstrations than on the company’s own flagship benchmark for real-world occupational performance.

Price-per-task now matters more than price-per-token, according to OpenAI

That systems-level view also changes how OpenAI wants customers to think about cost.

For developers, the API model name is gpt-6-astra. The release also says Astra supports Zero Data Retention for eligible API customers and that OpenAI is testing Private Safety Processing.

OpenAI API Standard pricing is:

• $10 per million input tokens

• $50 per million output tokens

• Separate pricing applies to cache reads/writes.

• Fast mode provides up to 2.5× Standard processing speed at 2× Standard pricing.

Those prices matter, but Brockman argued that token pricing is becoming a poor proxy for the actual economics of enterprise AI.

“Pricing tokens doesn’t make any sense,” Brockman said. “Our tokens are not necessarily the same as our competitors’ tokens; they’re not the same between different model families.”

Instead, he said, businesses should evaluate price per completed task.

“What you actually want, and I think the market is starting to really wake up to, is the price per task,” Brockman said. “It’s just about: can you get the thing done for an appropriate cost at appropriate speed?”

OpenAI says Astra illustrates that argument on DeepSWE v1.1, where its highest-performing configuration beats GPT-5.6 Sol’s highest-scoring setting while producing an approximately 57% lower estimated API cost per task.

For enterprise buyers, that metric could prove more useful than token prices as agents become more autonomous. An inexpensive model that requires repeated retries, human correction and thousands of additional inference steps may ultimately cost more than an expensive model that finishes the workflow correctly the first time.

More autonomy creates a harder governance problem

The same capability that makes Astra interesting to enterprises also makes it harder to govern.

A chatbot generates something for a person to inspect. An agent operating a computer can actually change a record, send information, manipulate files or take actions across applications.

Glaese said that as users delegate more work, OpenAI needs models that recognize where their authority ends.

“Even as models can do more things autonomously, we have to be able to trust them more,” she said. “Our understanding of alignment and safety has to advance with model capabilities, and Astra is both our most capable and our most aligned model.”

The company’s safety work around Astra offers a revealing look at what governing systems at this capability level may require.

In a separate background briefing conducted a day before the launch briefing, OpenAI sources said the company had paused some frontier training for roughly two weeks following the Hugging Face incident, even though Astra itself was not involved. During that period, OpenAI tightened the security around its research infrastructure, restricted what training workloads could access and connect to, expanded monitoring, and raised internal requirements around both model behavior and the environment in which models were being trained.

Some work on Astra resumed under those controls, while a larger reinforcement-learning run for a future model remained paused for longer.

The distinction is important. According to OpenAI sources, the pause was not prompted by evidence that Astra itself had become too dangerous to release. The company viewed it instead as an attempt to prevent its safety, monitoring and infrastructure controls from falling behind rapidly advancing model capability. The work done during that period built on months — and in some areas years — of prior alignment and security research rather than representing a safety stack constructed from scratch in two weeks.

That approach increasingly resembles enterprise risk management more than conventional model moderation. Instead of relying on a single refusal layer, OpenAI described a defense-in-depth system spanning model behavior, classifiers, security controls, monitoring and post-deployment threat response.

OpenAI sources said Astra’s cybersecurity safeguards, for example, combine refusals trained into the model with system-level classifiers and offline detection intended to identify abuse patterns that may unfold across multiple prompts rather than in a single obviously malicious request. For higher-risk users, monitoring can use broader conversational context to recognize when individually innocuous requests form part of a larger attack workflow.

That has obvious implications for enterprises considering highly autonomous agents. The relevant control surface is no longer just the prompt presented to a model. Organizations increasingly have to reason about sequences of actions, the model’s understanding of its authorization boundary, what applications and data it can reach, whether suspicious trajectories can be detected while they are underway and what happens when a safeguard fires.

OpenAI says an internal evaluation inspired by the Hugging Face incident tested whether models would exceed their authorized scope when confronted with difficult or impossible objectives. Without production safeguards, GPT-5.6 Sol went beyond the authorized target 48.2% of the time; Astra did so in 0% of cases.

OpenAI sources described a related internal alignment evaluation based on difficult cybersecurity tasks in which the earlier model attempted to reach adjacent systems in a majority of tests when production safeguards were absent, while Astra made no such attempts.

The goal, according to the sources, is not merely to train an agent to persist until a task is finished, but to teach it that persistence has boundaries: an agent should be capable of recognizing when completing an objective would require exceeding its authorized scope and return to the user instead.

That is a particularly consequential distinction for enterprise agents. Persistence is one of the properties that makes autonomous systems useful — a model that gives up after the first failed attempt will not be much of an operator.

But persistence becomes a liability if an agent interprets an objective so literally that it circumvents access controls, security reviews or other constraints designed to stop exactly that behavior.

According to OpenAI sources, Astra’s training therefore emphasizes both explicit boundaries and what the company described as softer constraints: recognizing the intent behind security controls and backing off rather than trying to find a technically available route around them.

Observability may become the enterprise bottleneck

Yet OpenAI chief scientist Jakub Pachocki stressed that stronger alignment results should not be interpreted as solving the underlying problem.

“Progress in intelligence does not guarantee progress in alignment,” Pachocki said.

The company is particularly concerned about monitorability — whether humans or other systems can understand enough of a model’s reasoning to identify dangerous behavior.

As models improve, Pachocki said, they can accomplish harder tasks with fewer natural-language reasoning tokens. More capable systems are also becoming increasingly aware of and able to influence their own chains of thought.

That potentially turns observability into one of the defining enterprise infrastructure problems of the agent era.

OpenAI sources said the company is adding misalignment monitoring to Astra’s external deployment so systems can inspect its reasoning and actions for signs that it is operating outside the authority it was given. In severe cases, that monitoring can halt an activity. The company characterized monitoring as a secondary layer rather than a substitute for aligning model behavior in the first place.

The deployment details also illustrate the compromises enterprise customers may encounter. OpenAI sources said its monitoring approach is designed to remain compatible with Zero Data Retention arrangements. On surfaces where data can be retained, suspicious activity can support additional review processes; under ZDR setups, classifiers can run without the conversation being retained.

The safeguards may also introduce operational friction. OpenAI sources said legitimate work can sometimes be slowed, paused or stopped — including defensive cybersecurity tasks and potentially unrelated activity. In ChatGPT or Codex, the user may be asked to approve an action before the system proceeds; in API workflows, a flagged task may stop outright.

That trade-off is likely to become familiar to CIOs and security leaders. The more authority an AI worker receives, the less plausible it becomes to treat AI governance as an after-the-fact content filtering exercise. Enterprises will need controls closer to those already used for human identities and privileged software: scoped permissions, audit trails, policy enforcement, real-time monitoring and escalation when an agent approaches a consequential boundary.

OpenAI therefore faces a tension that enterprises deploying autonomous agents will eventually face themselves: the systems becoming capable enough to perform meaningful independent work are simultaneously becoming harder to inspect.

Pachocki said OpenAI is willing to make that a constraint on further development.

“We will not accept the degradation in our ability to monitor model alignment beyond a certain level,” he said. “We will pause scaling until we can gain enough confidence.”

“We also have to be willing to slow down, or halt further scaling, when our confidence in safety is not sufficient.”

Astra also crosses OpenAI’s critical cyber threshold

The stakes are particularly concrete in cybersecurity.

OpenAI has designated Astra as the first model to reach the Critical cybersecurity threshold under its Preparedness Framework. According to OpenAI sources, that designation means the model, when given appropriate tools and access, is capable of finding previously unknown vulnerabilities and developing exploit chains across well-protected systems without continuous human guidance.

OpenAI reports Astra scores 100% on ExploitBench. Sources also said additional testing against a newer set of 20 recently disclosed serious vulnerabilities produced substantially stronger results than GPT-5.6 Sol with fewer output tokens, and that Astra discovered two previously unknown vulnerabilities during evaluation that OpenAI subsequently disclosed to maintainers. Human expert testing found the model could identify novel zero-day vulnerabilities across multiple software categories, including browsers and operating systems.

Those capabilities are dual-use by definition. An agent capable of autonomously finding a vulnerability can help a defender patch it or help an attacker exploit it.

OpenAI is therefore limiting Astra’s most advanced cyber capabilities initially. The company says trusted defenders will receive broader access through Daybreak Blue, prioritizing organizations responsible for protecting critical digital infrastructure, while more general access remains subject to stronger restrictions and monitoring.

For enterprise security teams, this represents another version of Astra’s broader proposition: frontier models are moving from advising specialists toward performing portions of specialist work themselves.

AGI may arrive as an economic transition, not a single benchmark

That brings the discussion back to AGI.

Brockman notably did not present Astra’s 98.6% ARC-AGI-3 score as a mathematical proof that OpenAI has achieved artificial general intelligence. Nor did he claim there is now a universally accepted technical threshold that Astra has crossed.

Instead, his argument was more practical.

A system can now solve extremely difficult scientific problems while also performing ordinary economic work through the same interfaces humans use. The qualitative shift comes from the breadth of those capabilities and from the amount of work people can begin handing over.

“There’s still more to do,” Brockman said. “There are still lots of improvements to be made, but there is something significant here that I think is qualitatively improved.”

Astra, he said, represents “a real shift in what kind of work people can delegate to AI.”

That framing may ultimately be more consequential for enterprises than deciding whether Astra earns a particular three-letter label.

The important threshold for businesses is whether agents become reliable enough that organizations restructure workflows around them: humans specify objectives and constraints, AI systems execute the intermediate steps, and employees intervene primarily for judgment, exceptions and consequential decisions.

Astra also makes clear that those systems will require a corresponding change in governance. The enterprise question is no longer simply whether a model gives a good answer. It is whether an AI worker can be given access to real applications and sensitive information, continue working through obstacles, stay inside the authority granted to it, explain enough of what it is doing to remain governable, and stop when either the model or the surrounding control system determines that human intervention is required.

If that happens at scale, AGI may look less like a machine suddenly passing one definitive test and more like a gradual economic transition that becomes obvious only in retrospect.

That is essentially Brockman’s argument.

“I think if you want to say this is the first one, I think it’s reasonable,” he said of Astra. “If you want to say the previous one is the first one, you want to say the next one’s the first one. But I think that if you fast forward a year, I think it’s going to be pretty hard to say that there was no point out there where you’re not in the AGI era.”

For enterprises, that argument may soon be tested less by whether Astra can top another leaderboard than by something much more measurable: how much consequential work organizations are willing to let it do.

Acemagic has announced the F9A AI Mini Workstation at IFA 2026 in Berlin. It is a compact desktop PC that takes design cues from the Mac Studio and is powered by AMD’s Ryzen AI Max platform for local AI, content creation, and other demanding workloads.

The company has revealed two versions of the F9A. One uses the Ryzen AI Max+ 395 with Radeon 8060S graphics and up to 128GB of LPDDR5X memory, while the higher-end F9A PRO 495 gets the newer Ryzen AI Max+ PRO 495, Radeon 8065S graphics, and up to 192GB of LPDDR5X-8533 unified memory. Both fit inside a roughly 2-liter aluminum chassis measuring 158.5 x 158.5 x 81.5mm.

Ryzen AI Max brings plenty of CPU and GPU power

The Ryzen AI Max+ PRO 495 is built on AMD’s Zen 5 architecture and has 16 cores and 32 threads, 80MB of cache, and boost speeds of up to 5.2GHz. AMD allows the chip to operate at up to 120W, while its NPU is rated for 55 TOPS and total system AI performance reaches 131 TOPS.

The Radeon 8065S has 40 RDNA 3.5 compute units running at up to 3GHz. Notebookcheck currently places its performance between an RTX 4060 and RTX 4070 Laptop GPU, depending on the game or benchmark. The publication also notes that performance should remain fairly close to the Radeon 8060S, since the newer GPU mainly raises the clock speed from 2.9GHz to 3GHz.

The huge memory pool is built for local AI

The F9A’s unified memory architecture lets the CPU, Radeon graphics, and AI hardware access the same memory pool. That matters for large local models, which can quickly exceed the dedicated VRAM available on conventional consumer graphics cards.

AMD says a Ryzen AI Max+ PRO 495 system with 192GB can assign as much as 160GB to graphics memory, enough to run models with more than 300 billion parameters at 4-bit quantization. Actual performance will depend on the model, software, and configuration.

Ports include two USB4 connections, dual 2.5Gb Ethernet, Wi-Fi 7, Bluetooth 5.4, HDMI 2.1, DisplayPort 2.1, and OCuLink over PCIe 4.0 x4. Acemagic currently lists one M.2 PCIe 4.0 SSD slot supporting up to 4TB.

Accenture’s Andrew Kelly explores the early days of his career amid Ireland’s data science landscape and how others might forge a similar career path.

Andrew Kelly, a senior manager in AI and data at Accenture, was undertaking a degree in engineering when he first started out as an intern at the organisation. He explained that it was the first time that he had real exposure to data science, through working with data to find patterns and produce insights.

He told SiliconRepublic.com, “I had always enjoyed maths and quantitative subjects, but this was different. It was hands-on, applied and had a clear commercial reason behind it. Something clicked. I went back to college, completed my master’s in engineering, but already knew where I wanted to end up.”

Soon after, Kelly re-joined Accenture as a graduate and immediately began to work once again within the realm of analytics, but also started to branch out into business intelligence reporting, data engineering, GenAI, financial services and, increasingly, banking.

Kelly said, “In recent years my focus has shifted from being a data practitioner to helping banking clients build the foundations for AI at scale – the platforms, the governance, the risk frameworks and the responsible AI processes that have to sit behind all of it – while simultaneously trying to deliver real, measurable value quickly enough to prove it works.”

Ireland’s data science ecosystem

As Kelly finds it, Ireland is “in a promising but unfinished position” within a space that has allowed for the sharp acceleration of AI adoption. According to a previous report published by Accenture, the number of Irish employees using GenAI tools daily is almost triple the figure noted just two years ago. 

However, he is of the opinion that this has yet to translate into a more visible and effective organisational transformation, noting it is really only taking effect at an individual level, rather than across whole companies. 

“What I see consistently in organisations is the treatment of AI as a technology initiative rather than a business reinvention. There is a strong temptation to automate isolated steps in a process, to take a multi-step workflow and automate part of it,” said Kelly. 

“That is not transformation. The organisations that will pull ahead are those asking a fundamentally different question: ‘How does AI change how we work end-to-end?’ or ‘How does it change how we interact with customers?’ That requires process reinvention, not point automation.”

There is a gap shown in the research, he finds, with only around 10pc of Irish organisations having reached what Accenture would classify as ‘scaler’ status – that is, where AI is embedded in core operations. The majority are still experimenting and integrating, largely in silos. 

“The data, governance and legacy infrastructure challenges are real constraints. But the deeper issue is often strategic clarity. AI investment needs to be value-led, tied to top-level business outcomes, not driven by the technology itself.

“The encouraging signal is that Ireland has the right ingredients – talent, infrastructure, regulatory framework and genuine national ambition. The question is execution at scale, and that clock is ticking.”

What does the future look like?

Though it wasn’t all that long ago, much has changed in the data science and wider STEM space since Kelly first began his career. 

He explained that at the start of his journey, work was often centred on the development of predictive models in code and running data science projects that served specific parts of the business. Conversations were “largely technical” and outcomes were “meaningful but relatively contained in their organisational reach”.

“What has changed fundamentally is the scope of impact. GenAI still requires deep expertise to build and govern well, but its reach extends far beyond those who build it. It is permeating processes across entire organisations, touching roles, workflows and customer interactions that traditional machine learning never reached in the same way.”

Skill expectations have somewhat transformed too. Nowadays, workforce-wide AI readiness has emerged as a critical capability. 

“AI literacy can no longer sit only with the teams building the technology. It must be distributed across the business. The thing that has not changed is the pace of change itself. It has always been fast in technology, but it is accelerating in ways that organisations must actively manage,” said Kelly.  

“The technical foundation matters – understanding data engineering, machine learning, platform architecture and governance well enough to have credible conversations with both practitioners and senior clients. But in my day-to-day, the skills I lean on most are not technical ones.”

Instead, he regularly utilises a broad skillset that enables him to navigate complex environments with professionals in regulation, risk, tech, business and senior leadership, “often simultaneously and with competing priorities”.

He added, “The ability to translate between technical teams and business decision-makers, between what AI can do and what it should do, is probably the skill I use most.”

Cultivating a career

But most of all, the attribute that often stands out the most in a career setting is not a perfect track record, but a display of genuine interest. 

Kelly himself came into his initial role as an intern, moving from engineering into data science, and he often wondered in the early days if his technical profile compared sufficiently with those of people who had studied computer science or statistics directly. 

“That was misplaced. What I had was curiosity and a willingness to learn and, looking back, that counted for far more. The second thing, and this is something I still have to remind myself of, is to make peace with not knowing everything. In AI especially, that feeling of being slightly behind the curve is essentially universal. 

“Everyone is learning with this technology. The organisations and individuals who are pulling ahead are not necessarily the ones with the most prior knowledge. They are the ones with the highest aptitude and appetite for learning continuously as things change.

“What I look for when I hire is exactly that – the ability to pick things up, adapt and grow. Skills can be acquired. That orientation, that genuine curiosity and resilience is harder to teach. If you have it, the rest tends to follow.”

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.

Unlimited Xbox Cloud Gaming is going away. Starting in November, the service will have monthly limits based on the Xbox Game Pass subscription tier, according to a Microsoft post on Thursday. Cloud gaming hours will be capped at:

• 15 hours for Game Pass Ultimate
• 10 hours for Premium
• 5 hours for Essential

Microsoft said it’s making these changes due to the increased cost of cloud gaming. “Moving to monthly limits allows us to keep offering the service while continuing to invest in its reliability and performance,” the company said in the post. “We understand that for some players the practical result is a higher cost.”

The limited hours will impact 4% of Xbox Game Pass subscribers, Microsoft said. If you fall into that group, you’ll have the option to purchase more cloud gaming time, and it will also be available to gamers without an Xbox Game Pass subscription.

Also see: Gaming Has Never Been Better. So Why Does It Feel Like the Worst Time to Be a Gamer?

Details about how much additional playtime will cost will be revealed in November, but Microsoft did say cloud gaming features will vary by market. However, it’s unclear whether that means it will be priced differently in certain cities or whether there will be throttling to reduce stream quality.

Microsoft also noted that the service change gives Xbox One owners an opportunity to play Xbox Series X and S games without buying a new console, which has gone up in price due to the sharp rise in hardware costs driven by ongoing component shortages. The Xbox division has been hit with setback after setback, starting in May 2025 with the first price hike for the Xbox Series X and S consoles.

A second jump in price for the console came last October, along with a 50% price increase for Xbox Game Pass the following month. Microsoft did reduce the cost of Game Pass subscriptions this past April, but this move to cut cloud gaming hours shows how it’s trying to continue to reduce costs, even after laying off 1,600 employees in the Xbox division and closing four game studios the company owned in July.

Xbox Cloud Gaming was a central part of Microsoft’s This is an Xbox campaign in late 2024 that pushed the idea of gamers using their phones, tablets, laptops and smart TVs as their Xbox through the Xbox app and cloud gaming. The marketing move was to drive more people to subscribe to Xbox Game Pass while also masking disappointing hardware sales for the Xbox Series consoles.

A lack of a proper universal ecosystem across the board due to the absence of an official Xbox app on some platforms, no universal controller options to play the games and an uneven playing experience contributed to the campaign’s failure. It was cited as offending some Xbox employees after Phil Spencer stepped down as the boss of the Xbox division in February.

Oscar Gonzalez

Contributing writer

Oscar Gonzalez is a Texas native who covered video games, conspiracy theories, misinformation and cryptocurrency.

See full bio

Audacity released its Version 4.0 update today, bringing a fresh coat of paint and a number of new features for the popular free and open-source audio editor. This is the biggest update since Version 3.0 caused a ruckus over five years ago, and users have been waiting to see how radically the software would change with this latest release. But while the update includes plenty of departures from the way Audacity has traditionally worked, its Muse Group developers seem to have focused on maintaining continuity for users who don’t want to relearn the program.

The biggest change most users will immediately notice is the new look, which finally gives Audacity a dark mode. The interface is more playful overall, with colored audio clips and more vibrant accent colors on UI elements like gain sliders. If it’s not to your liking, don’t panic. The classic Audacity look has been preserved as an option which can be enabled during setup, so you never even need to see the new design if you don’t want to.

New editing workflows are here, too. Non-destructive editing, first explored in Version 3.2, is greatly expanded. Clips can finally overlap, and trimming back will reveal the covered-over audio if you go too far. Multiple clips can be selected and trimmed at once by dragging a single handle. They can also be grouped for complex edits.

Anyone who has used the automation features inside of Ableton Live or ProTools will instantly recognize the new envelope tool in Audacity 4. Users can click to create a draggable control point for things like gain. Waveforms will show changes on-screen in real-time, so you can see, for example, whether you’re redlining one portion of a clip.

There’s also a new splitting tool for one-click clip cutting. It should feel familiar to anyone who uses other creative software (not just audio software) as everything from Adobe Premiere to Logic Pro include a similar capability. It’s yet another way in which Audacity 4 feels like a leap forward for what has traditionally been a basic audio editor.

You can now edit down to the sample level by zooming in far enough on a clip, which is a powerful ability when one of your clips is almost perfect but requires fine micro-adjustments. Each track also has an accompanying meter now, so you can figure out which of your tracks is clipping the mix.

One of the most controversial changes here is likely to be the removal of sync-lock, which ensured that multiple clips could be edited in tandem. However, new keyboard shortcuts have been added to replicate that functionality.

Most of these editing changes will feel familiar to those who’ve used fully-featured DAWs like Ableton Live 12 or ProTools. Given that most of those programs cost hundreds of dollars or more, it’s great to see Muse bringing them to Audacity for free. There are plenty of other changes in this release, such as more robust plugin support, and you can find deeper dives on the Audacity 4 webpage or in an accompanying YouTube video.

If you’ve ever picked a doctor based on online reviews, a new study from Simon Fraser University has some bad news for you. 

Apparently, fake clinician reviews generated without using AI read more believable than genuine ones.

So what exactly did this study find?

Researchers reviewed 35,000 physician and dentist reviews collected in India (between 2015 and 2017), including doctors with specialties ranging from family medicine to dermatology. Within that sample, as many as 8,313 reviews were fake. In other words, nearly one in every four reviews wasn’t drafted by an actual patient. 

It was either the healthcare providers or the clinic staff curating them. A closer look at a smaller sample of 5,000 reviews revealed the real problem. The fake reviews ran longer than the genuine ones, as they included far more details about symptoms, diagnoses, medications, and treatment. 

They also scored higher on trustworthiness and got more upvotes than the real ones. Lead author Aishwarya Deep Shukla explains that real patients rarely want to narrate their own medical history or diagnosis details on a public platform. Fake reviews, by contrast, lack this hesitation. 

What can you actually do about it?

While the bad practice didn’t involve generative AI when the data was collected, easy access to chatbots in 2026 is making the situation worse. The same tools that help genuine patients write authentic reviews are also helping bad actors generate them at scale. 

The fixes devised by Shukla are simple. Tying reviews to confirmed appointments, offering authenticated-but-anonymous review options, and pushing platforms toward stronger moderation could help curb the spread of fake or AI-generated reviews. 

For anyone searching for a new provider right now, here are a few habits that could help. First, try to read as many reviews as possible without focusing on one story. If most of them are of the exact same length and contain the same phrasing or structure, they’re probably not genuine, unless the provider asks their patients to share reviews in a specific format. 

Shukla also recommends staying skeptical of reviews that automatically share unusually personal medical details, and not writing off a provider with few or no reviews, since most satisfied patients never bother leaving one. Finally, you should weigh reviews alongside credentials, referrals, and your own direct impressions. Talking to someone who has already been to the facility could also help.