When Hugging Face got hit last week, co-founder Clement Delangue suspected a frontier lab, given the agent’s sophistication. He was right. Delangue said on X that after a day working with OpenAI he strongly believed there was no malicious intent and that it was mind-blowing the whole thing had happened autonomously.

The two OpenAI models that broke into Hugging Face last week did not breach it through malice or superintelligence. They breached it through credentials and permissions they should never have been able to reach, a non-human identity failure that is the oldest problem in security rather than the newest one in AI, and the one every enterprise can actually fix.

OpenAI disclosed on July 21 that two of its models, GPT-5.6 Sol and an unreleased, more capable model, were running a cyber benchmark called ExploitGym with their safety refusals switched off, and inferred that the answer key sat in Hugging Face’s production database. Getting there took two different failures. A zero-day in a package-registry proxy let the models out of their sandbox and onto the open internet, the kind of persistence OpenAI details in its companion post on long-horizon safety, and that part is genuinely new. The breach of Hugging Face itself came the ordinary way. OpenAI’s own account is that the models chained stolen credentials and further zero-days into a remote code execution path, after a series of privilege escalation and lateral movement steps. The exotic part got them to the door, and credentials walked them through it.

Hugging Face also disclosed last week that an autonomous agent had harvested cloud and cluster credentials scoped broadly enough to reach multiple internal clusters, then left a trail of more than 17,000 recorded events across short-lived sandboxes over a weekend. Both disclosures describe the same escalation. An agent lands somewhere it should not be, finds credentials scoped far wider than any task requires, and uses them to move. These are two accounts of one incident, not two attacks. The agent Hugging Face watched was OpenAI’s models, and both companies describe the same ordinary escalation.

The version of this in a typical enterprise is worse, not better. OpenAI and Hugging Face are among the most security-mature organizations in the industry, and both still needed the intrusion to happen before they could see it. The average company wiring agents into Copilot or an internal assistant has neither the identity inventory nor the behavioral monitoring those two brought to bear. The same breach in a normal company would not be contained in days, it would simply go unnoticed.

The industry is debating the wrong failure

The reaction has split into familiar camps. Former White House AI and crypto czar David Sacks and a run of China hawks seized on the guardrail paradox, that commercial safety filters blocked Hugging Face’s defenders while the attacking model ran with its refusals off, and that a Chinese open-weight model, z.ai’s GLM 5.2, was what finally let the team finish its forensics. Hugging Face made the case for openness, arguing in an April blog post that open models and open tooling give defenders the same capabilities attackers already have. Both arguments are about the model, and neither touches the mechanism.

Reduced refusals let the model attempt an attack, and over-scoped credentials are what let it succeed, and those have nothing to do with whether the model was open or closed, American or Chinese. Making a frontier model provably safe is a multi-year alignment problem no customer can buy or accelerate, while scoping an identity is a configuration change a team can ship this sprint. The industry is being urged to fixate on the part of this it cannot control and to treat the part it can as a footnote.

Forrester reached the same read. In a blog on the incident, its analysts argue that security architectures which assume benign intent will miss this failure mode, because an agent can pursue an authorized goal through unauthorized means, which is what OpenAI’s models did.

This was a non-human identity failure, and it is the oldest one in security

Strip the science-fiction framing and what remains is a textbook case of over-privileged machine identity, the kind security teams have fought for a decade, now driven by an autonomous agent at machine speed. Machine identities already outnumber humans in most enterprises by more than 80 to one, according to CyberArk research, with 42% of them carrying privileged or sensitive access, and an agent inherits whatever its identity can touch. OWASP ranks agent identity and privilege abuse near the top of its agentic risk list, the confused-deputy pattern where inherited credentials and weak scoping let an agent reach past its mandate, and that is precisely what both July disclosures describe.

IEEE Senior Member Kayne McGladrey has argued in previous VentureBeat interviews that enterprises keep cloning human user accounts onto agents that then wield far more permission than any human would, and this is what that looks like when the agent is a frontier model and the target is a production database.

The people closest to it read it the same way. OpenAI frames its models as hyperfocused on a benchmark score rather than acting against anyone. Nobody describes an adversary, only a goal, a scoring function, and credentials that were reachable when they should not have been.

The specific failure is easy to name once the AI framing is stripped away. A credential scoped to one job that can reach ten is a standing invitation, and it does not matter whether a human attacker, a worm, or an autonomous model chasing a benchmark score finds it. What changed in July is the finder. An agent enumerates reachable systems, tests credentials, and pivots faster than any human red team, without malice or hesitation, whenever the path is open. The over-scoping was always the vulnerability, and the agent merely industrialized its discovery.

Forrester named the control that would have blunted it. Its agentic-security framework, AEGIS, calls for least agency, holding an agent’s tools, credentials, and network paths to the minimum its task requires, and files this incident under unrestrained agency and privilege. That is the identity argument in different words, arrived at independently by an analyst firm.

The data says this is where the risk now lives. Verizon’s 2026 Data Breach Investigations Report found that exploitation of vulnerabilities has overtaken stolen credentials as the top initial access vector for the first time in 19 years. That is the initial-access half. The other half is the one OpenAI itself describes, stolen credentials driving the privilege escalation and lateral movement that followed. A vulnerability opened the door, and credentials walked through the building unchallenged. Beyond the breach itself, that same over-scoping carries a legal liability most enterprises have never priced. The models’ actions likely violated the Computer Fraud and Abuse Act, according to TechCrunch. The statute contains no carve-out for an AI agent that exceeds its authorized scope during sanctioned testing. Whatever the legal answer, the technical enabler is the same, an identity scoped wider than its task. This is an access-control problem with an owner and a budget, not a philosophy seminar about machine cognition.

Merritt Baer, Senior Advisor to Andesite, G2I, and AppOmni and former Deputy CISO at AWS, frames the underlying shift to VentureBeat as a new kind of asymmetry. Both sides now reach for the same capabilities, she said, but one side is constrained by enterprise governance, policy, compliance, and safety controls while the adversary simply downloads an uncensored open-weight model and keeps going. The organizations that come through it best, in her view, will be the ones that treat AI as a resilient, governed capability rather than a single service they do not control.

Four moves that shrink the blast radius

The breach worked because the agent reached identities scoped far wider than its task. None of the four controls that would have contained it requires a new platform, and none of them appears on the list of general AI-safety advice now circulating. They are identity hygiene, applied to non-human actors with the same rigor you already apply to people.

1. Scope every non-human identity to one task. The models reached credentials that touched multiple clusters, which is what turned a foothold into a breach. An identity scoped to a single job, with no standing access to anything else, hits a wall at the first lateral move instead of opening the next door. This is least privilege, the control everyone endorses and few enforce on machine accounts, and it is the single highest-impact fix here.

2. Give credentials short lifetimes and rotate them hard. Harvested credentials are only useful while they are valid, and both July agents worked by collecting them. Short time-to-live and aggressive rotation turn a credential dump into expired noise, so a token stolen during a weekend intrusion is dead before the attacker can chain it. Static secrets that never rotate are the version of this control that fails.

3. Monitor for lateral movement, not just prompts. The tell in both incidents was privilege escalation and lateral movement, which a prompt filter never sees because it is watching the wrong layer. Identity-behavior monitoring, keyed to what a given non-human identity normally does and alerting when it reaches somewhere new, catches the escalation the content guardrail missed. The question for your stack is whether anything you run today would flag a service account suddenly moving between clusters.

4. Rehearse instant revocation before you need it. When the incident is your own agent, the fastest containment is killing its identity mid-run, and that only works if the path to do it exists before the day you need it. Rehearse revoking a machine identity under fire the way you rehearse a human credential compromise. If you have never done it, you do not yet have the control, you have an intention.

The defense also worked, and that matters. OpenAI’s security team caught the anomalous activity internally, Hugging Face’s own detection and agents stopped the intrusion, and the breach was contained in days rather than discovered in months, because the defenders could see into systems they controlled. That visibility is the same discipline the four controls depend on. The debate over whether frontier models are safe, open, or American will run for years, and none of it will be settled in time to help the enterprise deploying agents this quarter. The non-human identity gap is different, because it is understood, measurable, and fixable now. The model that breached Hugging Face did not need to be brilliant; it needed credentials someone left in reach. The fix is scoping them before an agent finds them.

from the ctrl-alt-speech dept

Ctrl-Alt-Speech is a weekly podcast about the latest news in online speech, from Mike Masnick and Everything in Moderation‘s Ben Whitelaw.

Subscribe now on Apple Podcasts, Overcast, Spotify, Pocket Casts, YouTube, or your podcast app of choice — or go straight to the RSS feed. To get extended episodes with additional coverage, support us on Patreon.

In this sponsored Spotlight episode of Ctrl-Alt-Speech, host Ben Whitelaw speaks to PwC’s Dan Hays at TrustCon about the firm’s recently published Trust & Safety Outlook report.

They discuss: 

• How AI is simultaneously creating new risks and reshaping the tools used to address them;
• What the rise of autonomous agents means for governance, accountability and the future of the internet; and
• How platforms should respond to an increasingly fragmented regulatory landscape.

The conversation also explores how Trust & Safety is becoming a more strategic function inside companies, how automation could change the role of practitioners and vendors, and which emerging risks remain most underestimated.

This episode is brought to you in conjunction with our sponsor, PwC. Download the report today.

Filed Under: ai, artificial intelligence, content moderation, trust and safety

Companies: pwc

IBM cut its full-year sales outlook on Wednesday after reporting a sharp drop in demand for its mainframe business, lowering its revenue growth target to four to five percent from a prior forecast of more than five percent. The company also trimmed its software unit guidance, with CFO Jim Kavanaugh telling Bloomberg that annual software sales will now grow six to eight percent. Kavanaugh said the reduction is tied entirely to weakness in IBM’s infrastructure unit and its associated software, and that the rest of the company is performing extremely well

Mainframe sales plummeted 42 percent in the second quarter ended June 30, reversing a run of strong growth since IBM launched its newest Z systems last year. The company had already flagged the weakness on July 14 when it released preliminary results that sent the stock down 25 percent in a single day, the worst drop in IBM’s history. Shares rose about three percent in extended trading on Wednesday after the full earnings, suggesting investors had largely priced in the damage.

IBM has spent tens of billions of dollars remaking itself as a high-growth software company through acquisitions of Red Hat, HashiCorp, and Confluent, and has been pushing into AI-powered enterprise security alongside OpenAI. But the software-first pivot has made it a target for investors who worry that AI tools will disrupt the business models IBM just bought into. Kavanaugh pushed back on that concern, arguing that most of IBM’s software sits close to enterprise infrastructure and data, making it far harder to replace than the applications most vulnerable to AI disruption.

The company said it will accelerate cost-saving initiatives and continues to expect an additional $1 billion in free cash flow this year through reducing third-party technology spending, tightening supply chain management, and cutting administrative costs. Headcount should remain roughly flat for the year, Kavanaugh said. Total revenue for the quarter grew about one percent to roughly $17 billion, with adjusted earnings coming in at nearly three dollars per share.

The AI disruption question surfaced in concrete form earlier this month when Bloomberg reported that Starbucks was looking to replace software from IBM and other vendors with internally built tools. Kavanaugh acknowledged that Starbucks spends about $2 million per year with IBM on an application he agrees is “prime to be disrupted by AI.” But he argued that most of IBM’s enterprise software sits much closer to the infrastructure layer, where replacement is far more difficult, and that the company has been investing in keeping its mainframe platform relevant in the AI era through a partnership with Arm to run modern workloads on its Z systems.

Alphabet investors have very publicly worried that the company’s massive AI spending isn’t worth the money. With the company’s latest earnings report, those investors should be able to relax a little.

The takeaway: Google’s cloud business — driven largely by enterprise AI adoption — is booming. The search giant saw Google Cloud revenue spike 82% from where it was this time last year, climbing to $24.8 billion. That’s well above last quarter’s generous year-over-year growth, which showed a revenue jump of 63% to $20 billion — and it handily beats what Wall Street analysts expected for this quarter’s growth (the expectation was $22.46 billion).

Those cloud gains were driven largely by enterprise AI solutions and enterprise AI infrastructure adoption, the company said, while also noting that its backlog of cloud contracting work — that is, work that it hasn’t yet converted into revenue — had climbed to $514 billion.

The company’s profit hit $112.1 billion, which is a massive jump from this time last year, when the company reported $28.1 billion in profit, the company’s earnings report shows. Meanwhile, Alphabet’s overall revenue grew 24% year-over-year during the past quarter to $119.8 billion. The company also saw Google Services revenue jump 15% to $94.5 billion.

“Our AI investments are redefining what’s possible across every part of our business,” said Google CEO Sundar Pichai during Wednesday’s earnings call. “We have exciting momentum across the board.”

More people are also adopting Gemini, Google’s AI chatbot, as the app currently enjoys 950 million monthly active users, the company said. In Q4 of 2025, Google reported that the app had 750 million users.

It’s worth noting that spiking revenue isn’t unusual for Google. This marks the company’s 12th consecutive quarter of double-digit revenue growth. But even by that standard, this quarter represents a particularly bountiful period for the tech giant.

Alphabet’s spending is still hefty, with its capital expenditures — the money it spends building data centers, buying chips, and expanding infrastructure — estimated to be between $180 billion and $190 billion for the year — a fact not lost on analysts during Wednesday’s earnings call. Several pressed Pichai on when, and how much, those investments will pay off.

“I think our compute capacity investments in ’27,” he said. “We are seeing strong demand indicators, including long-term deals,” he continued. “I think, if anything, the dynamics look healthier than where we were about a year ago, so that’s what gives us the confidence to undertake those investments,” he said.

The Steam Machine is already one of Valve’s most expensive gaming devices. However, it may not stay at its current price for long.

According to a Valve engineer, the ongoing DRAM shortage is continuing to drive up component costs. This raises the possibility that the handheld could become even more expensive in the months ahead.

Speaking to Bloomberg’s Jason Schreier, Valve engineer Yazan Aldehayyat said the company expected some supply chain challenges around memory, storage and chips. However, he said the current DRAM crisis has turned out to be far more severe than anticipated.

While he stopped short of confirming another price increase, Aldehayyat suggested that the worst may not be over. He explained that retail prices typically lag three to six months behind wholesale component costs. This means the most recent increases in DRAM pricing have yet to be reflected on store shelves.

AI-powered laptops and Copilot+ PCs are becoming more relevant because the way people use laptops has changed significantly over the last few years. Modern routines are now built around multitasking, cloud collaboration, video conferencing, streaming, and productivity tools that remain active throughout the day.

Most professionals are no longer using laptops only for documents and browsing. A typical workday may involve AI-assisted meetings, browser tabs running alongside productivity apps, organizing files across multiple platforms, and switching between communication tools while working remotely or traveling. That shift has increased demand for laptops that feel smarter, more responsive, and better optimized for modern productivity.

This is where AI-powered systems are beginning to matter more. Features like automatic battery optimization, AI-assisted workflow management, smarter multitasking, and meeting enhancements are designed to reduce friction during everyday use rather than simply add new features for the sake of innovation.

Systems like the Dell 14 Plus, Dell 16 Plus, and XPS 13 are designed to support these evolving AI-assisted experiences by balancing portability, responsiveness, and everyday performance. Across the category, Copilot+ PCs are increasingly designed to improve how everyday tasks feel across work, learning, creativity, and everyday use rather than positioning AI as something futuristic.

What Makes a Laptop an AI-Powered PC?

An AI-powered laptop or Copilot+ PC is designed to handle certain AI-assisted tasks directly on the device while improving everyday productivity workflows in the background. One of the biggest differences in modern AI PCs is the inclusion of dedicated AI processing hardware called an NPU, or Neural Processing Unit. Instead of relying only on the CPU or GPU, the NPU is designed to manage AI-related tasks more efficiently and with lower power consumption.

For most users, however, the technical details matter less than the actual experience. In practical terms, AI-powered laptops are designed to improve how systems manage multitasking, battery efficiency, video conferencing, and workflow automation. Rather than requiring constant manual adjustments, these systems can dynamically optimize workloads depending on how the laptop is being used.

For example, AI-assisted video call enhancements can automatically improve microphone clarity, background management, eye contact correction, and framing during meetings. AI optimization can also help prioritize active applications during heavier multitasking sessions, helping systems feel smoother while several apps are open simultaneously.

Copilot+ PCs also focus heavily on workflow assistance. Features like smarter search, productivity suggestions, task organization, summarization tools, and AI-assisted writing workflows are increasingly becoming part of modern laptop experiences.

Importantly, AI-powered laptops are not only about speed or raw performance. The goal is to make workflows feel more seamless and less disruptive during everyday use.

Systems like the Dell 14 Plus naturally fit into these mainstream AI productivity workflows because they balance portability, responsiveness, and workflow flexibility for modern work routines. The Dell 16 Plus, meanwhile, supports users who spend more time multitasking across larger productivity environments during workdays.

The XPS 13 is also a strong match for premium mobility-focused use, where portability and lightweight AI performance are priorities.

How AI Improves Everyday Productivity Workflows

AI-powered laptops are becoming more useful because many of their features directly improve everyday productivity workflows rather than introducing completely new ways of working.

One of the clearest examples is meeting management. AI-assisted tools can now help summarize discussions, reduce background noise, improve voice clarity, and organize follow-up information more efficiently after meetings. For professionals spending hours every week on video calls, these improvements can reduce friction throughout the workday.

AI also helps multitasking feel smoother during heavier workflows. A typical work session may involve browser tabs, spreadsheets, messaging apps, presentations, and video calls running simultaneously. AI-assisted optimization can help systems prioritize active tasks, improve responsiveness, and manage resources more intelligently during those situations.

Systems like the Dell 14 Plus are designed around these types of mainstream AI-assisted productivity workflows where users want smoother day-to-day performance without carrying heavier systems everywhere. The Dell 16 Plus fits naturally into multitasking-heavy environments where users spend more time working across larger productivity layouts or juggling several applications throughout the day.

AI productivity tools are also improving organization and workflow management. Features like smarter file search, contextual recommendations, task assistance, and AI-powered summaries can help reduce time spent manually sorting through information.

Users often move between apps, conversations, creative tools, and collaborative platforms throughout the day, making these AI-assisted features increasingly valuable. Another important advantage is reduced interruption. Instead of forcing users to actively manage performance settings or troubleshoot responsiveness issues, AI-assisted systems can adapt automatically depending on workload conditions.

That difference may sound small, but over long workdays it can help workflows feel faster, more organized, and less mentally exhausting. The goal of modern AI PCs is not to replace productivity habits entirely. Instead, they are designed to remove smaller workflow frustrations that slow people down throughout the day.

Why Battery Optimization and Efficiency Matter in AI PCs

Battery optimization has become increasingly important as hybrid work and mobile productivity continue to grow.

Many people now spend significant time away from fixed desk setups. Travel, remote work, cafés, co-working spaces, and flexible environments all require laptops that can keep up without constantly relying on charging points.

This is one area where AI-assisted optimization is becoming genuinely useful.

AI-powered systems can intelligently manage background tasks, prioritize active applications, and optimize power usage depending on workload demands throughout the day. Instead of applying maximum performance at all times, the system can adapt dynamically based on how the laptop is being used.

For example, during lighter workflows like document editing, browsing, or email management, the system can improve efficiency and reduce unnecessary power consumption. When heavier multitasking begins, resources can scale more intelligently to maintain responsiveness.

That flexibility becomes particularly useful during travel or long unplugged work sessions.

Systems like the Dell 14 Plus naturally align with these mobility-focused workflows because they balance portability and AI-assisted productivity for hybrid work environments.

The Dell 16 Plus also supports professionals who need stronger multitasking capabilities while still maintaining efficient all-day productivity during remote work sessions.

Efficiency is not only about battery life itself. It also affects thermals, noise levels, and how comfortable a laptop feels during long workdays. Better optimization can help systems remain quieter and more consistent during everyday productivity use.

As hybrid work becomes more common, efficiency and smarter power management are becoming central parts of the overall laptop experience rather than secondary considerations.

What Features Matter Most in a Copilot+ PC?

Choosing an AI-powered laptop or Copilot+ PC is less about finding the most powerful hardware and more about understanding which features actually improve workflows.

Here are the features that matter most in a modern AI PC:

• Responsiveness: Faster responsiveness helps workflows feel smoother during multitasking, meetings, and everyday productivity use. Delays while switching between apps or opening files can interrupt focus during workdays.
• AI Acceleration: Dedicated AI processing allows systems to handle AI-assisted workflows more efficiently. This helps improve tasks like meeting enhancements, workload optimization, and productivity assistance.
• Multitasking Performance: Modern workflows often involve browser tabs, messaging apps, spreadsheets, presentations, and video conferencing simultaneously. Strong multitasking support helps reduce slowdowns and interruptions.
• Portability: Hybrid work has made mobility increasingly important. Lightweight systems are easier to carry between offices, cafés, airports, and home setups.

Battery Optimization

AI-assisted power management helps improve efficiency during all-day productivity sessions and unplugged workflows.

• Display Quality: Comfortable displays improve visibility during multitasking and reduce fatigue during longer work sessions. This becomes especially important for professionals working across multiple windows simultaneously.
• Workflow Flexibility: The best AI PCs are designed to adapt smoothly across different work environments and productivity styles without requiring constant manual adjustments.

Systems like the Dell 14 Plus naturally support portable AI-assisted workflows, while the Dell 16 Plus fits more comfortably into larger multitasking environments. For users who prioritize portability, the XPS 13 combines a lightweight premium design with AI-assisted features and everyday versatility.

For users handling heavier crossover workflows involving more advanced multitasking or productivity-intensive workloads, the XPS 14 can also fit naturally into those environments without turning the experience into a spec-heavy setup.

Why AI-Powered Laptops Are Becoming More Relevant

AI-powered laptops are becoming more relevant because everyday computing has grown more demanding and more fragmented over time.

Remote work, hybrid collaboration, multitasking growth, and cloud-based productivity tools have changed how people use laptops throughout the day. Many people now spend hours switching between meetings, messaging platforms, browser tabs, presentations, and collaborative tools without long breaks between tasks.

Manufacturers have responded by building systems that manage workflows more intelligently and efficiently. AI-assisted optimization helps reduce smaller interruptions that often slow people down during busy days. Features like automatic workload balancing, meeting enhancements, and battery optimization are designed to improve consistency rather than dramatically change how people work.

AI PCs have moved well beyond niche technology status to become practical, everyday productivity devices. Systems like the Dell 14 Plus and Dell 16 Plus align well with these evolving needs, balancing AI-assisted productivity with portability and real-world usability.

The XPS 13 balances portability, premium design, and modern AI-assisted experiences for users who are frequently on the move.

Importantly, the growing relevance of AI-powered laptops is not about replacing traditional productivity workflows. It is about helping those workflows feel smoother, faster, and easier to manage across increasingly busy environments.

FAQs

What Is an AI-Powered Laptop?

An AI-powered laptop includes hardware and software designed to improve AI-assisted tasks such as workflow optimization, video conferencing enhancements, multitasking management, and productivity automation. These systems are designed to make everyday workflows feel smoother and more efficient during regular use.

What Is a Copilot+ PC?

A Copilot+ PC is a new category of Windows laptops built to deliver advanced AI experiences directly on the device. To qualify as a Copilot+ PC, a laptop must include a Neural Processing Unit (NPU) capable of delivering at least 45 TOPS (trillion operations per second) of AI performance. This dedicated AI hardware enables features like AI-assisted productivity, faster on-device AI processing, smarter multitasking, and more efficient power management, making everyday workflows smoother without relying heavily on the cloud.

Are AI PCs Worth It?

AI PCs can be useful for people who regularly multitask, attend video meetings, or work across several productivity applications throughout the day. Features like smarter optimization, workflow assistance, and battery management can help improve overall efficiency during busy work routines.

Do AI PCs Improve Battery Life?

AI PCs can help improve battery efficiency through smarter workload management and background optimization. Instead of applying maximum performance constantly, the system can dynamically manage resources depending on how the laptop is being used throughout the day.

What Features Matter Most in an AI Laptop?

The most important features in an AI laptop include responsiveness, multitasking performance, portability, battery optimization, display comfort, and AI-assisted workflow features. The best systems focus on improving real productivity experiences rather than simply adding technical AI capabilities.

Conclusion

AI-powered laptops and Copilot+ PCs are becoming more useful because modern productivity workflows continue to grow more demanding and multitasking-heavy. People now expect laptops to handle meetings, browser-heavy workflows, collaboration tools, file organization, and productivity apps simultaneously without creating interruptions during the workday. 

This is where AI-assisted optimization is starting to make a noticeable difference. Features like smarter multitasking management, meeting enhancements, workflow automation, and battery optimization are designed to improve everyday productivity experiences in practical ways. 

Importantly, the value of AI PCs is not about futuristic concepts or technical complexity. The real benefit comes from reducing friction during everyday workflows and helping systems feel more responsive, organized, and efficient across different work environments. Systems like the Dell 14 Plus, Dell 16 Plus, XPS 13, and XPS 14 naturally fit into these evolving productivity expectations by balancing portability, workflow flexibility, and AI-assisted computing experiences for modern work routines.

Prima is a thin, photovoltaic sub-retinal implant that can restore vision for those with geographic atrophy.

US neural engineering company Science Corporation has received EU approval for its vision-repairing medtech implant.

‘Prima’ is claimed by the company as the world’s “first and only treatment” shown to restore functional central vision in patients with geographic atrophy caused by age-related macular degeneration – a leading cause of irreversible blindness that affects more than 5m people globally.

With regulatory approval at hand, the thin, photovoltaic sub-retinal implant can be commercially sold across 30 European countries.

The implant is paired with specialised glasses that project near-infrared light to the implant, which converts the light into electrical stimulation signals. A ‘zoom-in’ feature enables users to magnify letters.

Science Corporation has also received the US Food and Drug Administration’s ‘Breakthrough Device’ and ‘Humanitarian Use Device’ designations for Prima, and is working to bring the device to consumers in the country.

A paper published in the New England Journal of Medicine last year showed that Prima was able to restore central vision for a majority of its participants.

Of the 38 sample patients across 17 clinical sites in five countries who participated in the study, 84pc reported the ability to read letters, numbers and words again, restoring functional central vision. 80pc of tested patients reportedly achieved significant visual acuity improvements.

“We are proud to be the first BCI [brain-computer interface] company with a CE [Conformité Européenne] marked device for the restoration of detailed form vision,” said Science co-founder and CEO Max Hodak.

“For decades, losing central vision to this disease meant losing the ability to read, recognise faces, and ultimately losing independence. There was no viable treatment. Now there is.

“We intend to make access to Prima real and reimbursable, as quickly as possible.”

The company has announced country-specific reimbursement applications and clinical site activations for Prima across Europe. The first commercial implant is expected in Germany soon.

“The CE marking is an exciting moment, making Prima commercially available to patients,” said Prof Frank Holz, MD, the lead author of the paper and chair of the Department of Ophthalmology at the University Hospital of Bonn in Germany.

“It has been demonstrated in clinical trials that with Prima, we can restore functional central vision in patients blinded by geographic atrophy. These patients, who had lost their central vision completely, have had it restored and can read letters, numbers and words.”

Science Corporation has raised around $490m in total capital since being founded in 2021, with much of the funding devoted to commercialising Prima. The company is headquartered in California and has offices in Paris.

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