For many people, using AI chatbots is incredible at first. It makes them feel productive, confident, maybe even smarter. But later, there’s a hangover. Perhaps there are problems with their memory, or certain skills, like writing or coding, don’t seem to be as sharp as they once were. Maybe they feel like they can’t solve problems or make decisions or remember facts as well as they could before they had the help of AI.
Tech & AI
ChatGPT brain isn’t inevitable: How to use AI without getting dumber
Susette Brooks, a marketing director living in Philadelphia, said she started using AI tools reluctantly but ultimately relented. She let AI write some of her emails, which lightened her workload, but then the dulling effects sank in. “I felt my writing and thinking decline as it was happening,” said Brooks, who has an MFA in nonfiction writing. “I had a harder time recalling information and synthesizing ideas.”
When you use external tools to reduce the cognitive effort required to do a task, you’re doing something called cognitive offloading. If you’ve ever used a calculator to do a math problem or used a calendar to remember an appointment, you’ve engaged in cognitive offloading. Tools like Claude and ChatGPT can do math problems and remember your appointments, but they can also write your term papers or create your work presentations. This is cognitive offloading at a scale we’ve never seen before.
Cognitive offloading isn’t necessarily bad. It’s actually what makes it possible for humans to be so productive. But as you outsource more and more cognitive tasks, you can start to become forgetful or see certain skills, like writing, start to decline.
There’s a term for this: de-skilling. This is an old economics term that has emerged as a bit of a buzzword used to describe the perceived downward spiral of competency that’s accompanied the proliferation of AI tools. And it certainly sounds bad, especially if you’re a novice who needs to build skills. De-skilling can affect your critical thinking, your writing abilities, and your creative process. It can also affect your recall, as relying too heavily on AI tools can make it harder to remember things.
- AI can make you more productive while quietly making you less capable on your own.
- Offloading more tedious or mind-numbing tasks is less likely to make you feel dumber.
- The long-term solution isn’t necessarily less AI. It’s being more intentional about the tasks you use AI for.
Young people, at least, are worried about the long-term consequences of frictionless thinking. A recent survey by Gallup and researchers from the University of Pennsylvania found that 68 percent of Gen Z adults in the United States are concerned that offloading too many cognitive tasks is robbing them of important skill-building. About the same percentage of those surveyed worry that AI discourages them from critical thinking and social learning. Some 37 percent of US adults under 30 say that AI will have a negative effect on them personally, according to a Pew Research Center study published in June. And yet, more than half of Gen Z Americans use AI on a weekly basis.
The paradox of AI is that, even if we feel like using these tools is bad for us, saving time and avoiding the friction of thinking is too tempting. We’re hardwired to seek out the path of least resistance, according to Daniel Willingham, a psychology professor at the University of Virginia, because cognitive activity is so taxing, and may or may not get you to the right solution. Memory, on the other hand, is efficient and effective — you know that something worked in the past, and it will work again.
“Our mind is really set up to save you from having to think,” Willingham told me. “Thinking is what you do when you’re in trouble.”
The good news is that experts say it’s possible to use AI in a way that helps you save time and protects your cognitive abilities instead of diminishing them. Here’s their advice.
It’s not your imagination — too much AI use can erode your skills over time
Derek Salyers, a content creator in Ohio, started using AI to get more done, faster. But over time, two things happened. First, the work got worse. “A lot of what I was putting out became AI slop, for lack of a better term,” Salyers told me. And then, his brain got worse, too.
“When you get used to the AI making the decision, you stop pausing to actually think about things,” Salyers said. “And if you were never really present with it, you don’t remember it later. I wasn’t forgetting things because my memory got worse; I was forgetting because I was never really thinking in the first place.”
This effect is showing up in classrooms, too. One study of nearly 1,000 high school students found that those using ChatGPT scored 48 percent higher on a math test than their peers who didn’t have access to the AI tool. But when they were tested again without the chatbot, their scores were 17 percent lower than the students in the control group who had never had ChatGPT access. Similarly, researchers at the University of California Irvine looked at longitudinal data from online tests starting in 2015, and found that students spent less time and performed better on the text-based questions after the introduction of ChatGPT. (ChatGPT is very good at helping with text-based questions.) But when those students were later given offline tests, without the ability to use AI help, they performed worse.
All of these students, it appears, were offloading the cognitive work to AI, which provided short-term gains. But in the long term, their thinking skills suffered.
Your basic cognitive abilities, like your working memory and selective attention, are quite resilient.
Evan Risko is a psychology professor at the University of Waterloo in Canada who co-authored the foundational paper on cognitive offloading a decade ago. He recently told me there’s not a lot of research into what’s going on in our brains when we’re cognitive offloading, but scientists are gaining a pretty clear picture of the pros and cons of doing it. The advantages vary depending on the task, but in general, cognitive offloading allows us to extend our imperfect memory systems, which you’ve probably done if you’ve ever set a reminder for yourself. The downside, Risko said, comes in offloading mental processes that are important. “Then we risk compromising our cognitive performance,” he said.
Your basic cognitive abilities, like your working memory and selective attention, are quite resilient. But as Risko and his colleagues explained in a paper this year — bluntly titled “Is AI making us stupid?” — other skills, like doing arithmetic and flying planes, are learned behaviors. You first have to acquire them and then, in order to retain them, you have to practice. AI use can compromise both the acquisition and retention of these skills — and people might not even realize this is happening to them, because AI makes them believe they’re more capable than they really are.
“People may have difficulty realizing that their skills are decaying or that they are failing to learn when using AI tools because the output and their performance with AI is at a high level,” said Brooke Macnamara, a psychology professor at Purdue University who co-authored the “Is AI making us stupid?” paper. “They may not realize that if the AI tool were unavailable how low their independent performance might be.”
Be intentional about the tasks you use AI for
The most important step to preserving your cognitive performance is to ask yourself if you should be using AI for a given task at all.
“Just because AI is a tool that’s available to us and it can accomplish a lot of tasks for us, it doesn’t mean that we should always use it,” Anjali Singh, a human-AI interaction researcher at the University of Texas at Austin, told me. Writing, for example, is something that a lot of people are now using AI for. But offloading that work means that you lose out on the independent thinking and reasoning that goes into expressing your thoughts. You also miss the chance to improve your writing skills if you don’t actually write.
“We keep outsourcing these kinds of skills to AI over time and do not engage in those activities ourselves, then the de-skilling occurs,” Singh told me. “And that is I think probably one of the main reasons people could be feeling that they’re getting dumber as they’re using AI over time.”
“We are not getting these moments to reflect.”
— Anjali Singh, Univeristy of Texas
It might seem counterintuitive, but it’s actually best to use AI for things you’re already pretty good at, the tasks you’re fully capable of performing yourself. A recent working paper by MIT economist David Autor and his colleagues (note: Several of them work for Google, who funded the study) describes how giving AI tools to 133 patent lawyers and then taking away those tools led to different outcomes, depending on the lawyers’ level of experience. All lawyers saw performance gains while using the AI tools, but those gains disappeared for the younger lawyers when the tools went away. Experienced lawyers, however, performed better afterward than their counterparts who never had access to AI tools.
A similar effect showed up in a recent experiment by Middlebury College researchers. In the first session, more than 200 undergraduates learned about an unfamiliar topic and then wrote an analytical essay, some with the help of an off-the-shelf AI tool. They performed a similar test a week later, except this time none of the students had access to AI. The students who had AI access improved across the board, but what’s telling is that there appeared to be two types of AI users: augmentation users, who used AI to improve their work, and automation users, who just had AI do the work for them. The augmentation group performed better than everyone.
“Using AI to deepen or develop one’s own thinking on or understanding of a topic, rather than letting AI do all of the thinking, seems like the best way to use these tools,” Lauren Richmond, a cognitive science professor at Stony Brook University in New York, told me.
Tell your chatbot to treat you like a student
If you, like me, have ever fallen down a rabbit hole while trying to understand the geopolitical nuances that led to the outbreak of World War I and ended up staring blankly at a mountain of text feeling more confused than when you started, you know that using AI to learn doesn’t always go as planned. You may have also had the experience of writing prompts or asking questions and then receiving a long, overly detailed response. That can initially seem satisfying, but it’s often too much text to digest at once, so maybe you ask another question — and get back another wall of text.
Older ways of accessing information, like reading books or even using Google to do research, happened more slowly, and that gave us time to process the information, Singh from UT Austin said. But that’s gone now. “We are not getting these moments to reflect,” she said.
If you find yourself drowning in content, that’s also a sign that you’re probably not expert enough in the material, or you’re not being intentional enough about your approach.
It starts in the settings. Here, you should explain that you’re trying to build skills, not offload them, and instruct the AI to challenge you and engage with you. If you’re using ChatGPT, you can plug in custom instructions by going into the personalization tools. Claude has similar options called Instructions for Claude.
If you’re explicitly trying to learn something new, you might also try some of the tools these companies have designed for this. OpenAI has study mode in ChatGPT. Anthropic offers Claude for Education. Google has Gemini for Students. Instead of simply providing answers, these tools act more like tutors, by asking questions, working through problems, and encouraging you to come up with solutions on your own.
Slow down and embrace friction
Something that’s both tempting and dangerous about AI is how fast it can do work. Vibe coding an app can take minutes, rather than days of manual coding. That’s very appealing if you have too much on your plate and want to get through it all quickly.
“The workload was so heavy and crazy, you have no time to think,” Joei Chen, a former brand director at a tech company, told me. “So you just throw stuff into AI and say, ‘What do you think?’”
At the same time, creating more code does not necessarily lead to more finished software, according to a recent Wharton School study. That increased productivity also doesn’t necessarily lead to happier workers. And the faster you move, the more critical thinking you’re going to offload. “Your boss asks you, ‘What does Claude think of this?’” Chen said, so AI “inadvertently becomes the final boss.”
Slowing down and forcing yourself to do more critical thinking is an easy way to hold onto some agency — not to mention some brain power. One easy way to do this is to make your interactions with AI more collaborative than transactional. For example, if you are going to use AI to help you write something, ask for five options of a draft instead of one, and then read each of them yourself and decide which is the best.
“Ask for feedback, hints, examples, explanations, advice, practice questions.”
— Evan Risko, University of Waterloo
“Just the simple act of comparing things stimulates a great deal of the critical thinking processes that we kind of care about,” said Advait Sarkar, an AI researcher at the University of Cambridge, “and has beneficial effects for things like developing your judgment, your own awareness of what you don’t already know, and stimulating your recall.”
The word that came up again and again as I talked to experts about how to use AI without getting dumber was metacognition, or thinking about thinking. A lot of AI users let metacognition take a back seat to whatever content is flowing freely from a chatbot. And it can happen fast.
While letting AI do all of the thinking for you is tempting, Matt Beane, an associate professor at the University of California Santa Barbara and author of The Skill Code: How to Save Human Ability in an Age of Intelligent Machines, told me that approaching AI this way “is your worst enemy.” To counteract this, you have to introduce more friction.
“You must be very aggressive about how you experiment with it,” Beane said. Risko, the cognitive offloading expert, echoed this advice. “Ask for feedback, hints, examples, explanations, advice, practice questions,” he said.
The best test for judging whether you’re offloading too much thinking to AI and the technology is chipping away at your skillset: Stop using AI and use your brain, and see what happens. You don’t have to stop forever, but you should, for example, spend a day writing emails without AI’s help to see if you’re as fluent as you used to be. Or revert to using Google for research and confirm that you can still find facts without a chatbot finding them for you. Build an app with your own code and see if it flows naturally.
You might be troubled to find that AI made you worse at something. But you’re not actually dumber than you were before, and you can rebuild your lost skills the old-fashioned way: read, write, do arithmetic. If something requires thinking, go ahead and think.
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Amazon has unveiled two new Bee “personal AI” devices designed to continuously record, transcribe, and analyze conversations: the $75 wearable Bee Frontier and the $100 phone-mounted Bee Eclipse. Amazon says the devices can generate conversation summaries, surface recurring themes, and recommend actions or purchases, while claiming audio itself is never stored and users can delete transcripts and summaries. CNBC reports: The Bee Frontier is an upgraded version of Amazon’s existing wearable that can either be worn on a wrist or clipped to clothing. Similar to earlier generations, the Bee Frontier has up to seven days of battery life, but it’s also more expensive, costing $75 compared with its $50 predecessor.
Amazon acquired Bee, a San Francisco startup known for its always-listening bracelet, in July 2025 for an undisclosed amount. Bee bracelets are equipped with artificial intelligence and microphones that can record, transcribe and analyze conversations. The dual mics on the Bee Frontier have upgraded noise filtering, “so it can take note of what matters and ignore what doesn’t, even in noisy environments like a crowded restaurant or open office,” Amazon said in a blog post.
Amazon also debuted the $100 Bee Eclipse, which functions like other Bee devices, but instead of being worn, the credit card-sized gadget magnetically snaps on to the back of a smartphone. The Bee Frontier is available Thursday, while the Bee Eclipse “will be available soon,” the company said.
Read more of this story at Slashdot.
Microsoft announced that Windows Server 2022 has reached the end of mainstream support and will remain in extended support until October 2031.
Microsoft unveiled Windows Server 2022 in March 2021 and made it generally available in September 2021 as a Long-Term Servicing Channel (LTSC) release with 10 years of support.
“As of October 13, 2026, Windows Server 2022 has reached end of mainstream support and transitioned to extended support. The October 2026 security update is the last mainstream support update available for this version,” it said.
“Extended support includes security updates at no additional cost, and Windows Server 2022 will continue to receive monthly security updates through October 14, 2031.”
Microsoft has also extended Windows Server 2022 hotpatching until October 2027, one year after the mainstream end date of October 2026, for systems running the Datacenter: Azure Edition.
Before Windows Server 2022 reaches the end of support, Microsoft advised IT admins to upgrade to Windows Server 2025, which began rolling out to Windows Insiders in January 2024 and reached general availability in November 2024.
“Windows Server 2025 is the latest Long-Term Servicing Channel (LTSC) release for Windows Server. To help keep your environment protected and supported, plan your upgrade path to Windows Server 2025 for full mainstream support,” the company added.
According to Microsoft’s lifecycle platform, Windows Server 2025 will reach its end of mainstream support in November 2029, with an additional five years of extended support until November 14, 2034.
Those who want to test it before deployment can use the free 180-day Windows Server 2025 trial available via the Microsoft Evaluation Center. You can find more information about Windows servicing dates on the Windows Lifecycle FAQ page or using the Lifecycle Policy search tool.
On Tuesday, Microsoft also announced that devices running Windows 11 24H2 Home and Pro editions no longer receive security and non-security preview updates and advised customers to upgrade to Windows 11 26H2 (the latest Windows 11 version, also known as the Windows 11 2026 Update).
Last week, it also warned users to upgrade devices running unsupported versions of Windows because they will stop receiving security updates after next year’s Windows Update certificate rotation.
Join Mikko Hyppönen and security leaders from the NFL, CHANEL, and Atlassian for a two-hour digital summit on what AI-speed attacks change, what defenders should stop doing, and how to validate, decide, fix, and re-validate at machine speed.
With the rise of AI pentesting, a lot of people are wondering if it’s as good as a human. Having started as a pentester twelve years ago, and having moved through similar roles to working on Intruder’s AI pentester,
I’ve come to understand the advantages and disadvantages of both. Here’s how they compare.
The strict methodology was never the good bit
You may expect AI pentesters to follow strict methodologies in the way that many human pentests do.
At Intruder, we experimented with this approach early on – structured systems where each agent had a specific vulnerability type to look for. What we found was that agents would miss things outside their remit entirely, or miscategorize what they did find as the one thing they’d been told to look for, causing confusing reports.
A complete lack of structure also failed to produce good results, as completely free agents without any guidance couldn’t break down and manage large applications properly. They could find interesting things, but at the cost of missing a load of other vulnerabilities in our targets.
We’ve put a lot of testing into finding the balance between these approaches, and settled on giving agents freedom with broadly defined roles to help them approach and manage large applications, and stricter guardrails to stop them causing damage.
This does mean that our AI pentester doesn’t tick off the parts of a methodology document that you’d typically see in a human pentest. That may make some people uncomfortable – a strict methodology gives people assurance that someone has gone through your application and tested everything on the list. An unauthenticated SQL injection needs to be found, and a checklist is there to make sure it does.
The methodology gives a lot of people the idea that a human pentest is a perfectly repeatable product, as any tester will be able to follow the methodology, and everything important will get found. The reality is a lot more nuanced than that though, as pentesting is a skilled job, and many of the most impactful findings on a pentest will come from the tester being experienced in noticing weird behaviours and following up on them.
For our AI pentester, we ended up with much more interesting and impactful results by not trying to guide our agents through specific vulnerability types. A good example is a complex authentication bypass the agents found that had been sitting in a codebase through five years of pentests without any human testers picking it up.
The mechanics of that issue mean that you’d never think to write a testing guide that could be strictly followed to identify it, and if we’d tried to limit our AI pentester too much it never would have found it either.
You could say that only finding the common things would still be pretty good, but there’s such an array of vulnerabilities that aren’t common that you find during a pentest. And just because they’re not common, it doesn’t mean that an attacker can’t find them too, especially now AI is being used to enable attacks more.
A strict methodology being carried out by a competent tester only ever provided a low baseline of reliability, and it didn’t even do that perfectly. What makes a pentest good is the part that a skilled tester does outside the checklist, and that’s where AI thrives – following its nose through your application and finding the things that were never going to make a list.
Intruder’s AI pentesting agents deliver the depth of a manual engagement on-demand: no lead time, no scoping calls, a fraction of the cost.
Test with every release, close your window of exposure, and get an audit-ready report in hours.
What they know, and what they can’t ask
Our AI pentests take a white box approach, which means the agents get the source code. They work through the entire codebase and build a picture of not just what every page is, but how the application is meant to function and what requests can be made. Doing this to the level of detail that the AI is able to isn’t feasible for a human pentester during a typical time window for a pentest.
The AI pentester also builds up a sense of what the application is for, and a good example of this is access control – it doesn’t need to be told that low privileged users shouldn’t be able to reach admin functionality, or that a particular bit of data is sensitive.
You can give them extra information as well, just like a human tester. For example, people often specify that they’re worried about their customers being able to access each others’ data, and the pentester has done a great job of focusing on these issues and identifying a lot of them.
What the agents can’t do is ask you for anything you didn’t already give or tell them. If I don’t understand what a part of an application does while I’m doing a pentest, I can ask my contact, and those conversations can be very useful for testing the application properly, and assessing risk in the final report.
I can also clarify the scope with my contact if needed, or ask for permission to verify something when I’m unsure if there’s a risk of disruption.
We’ve had to deliberately make our AI pentesting agents cautious on this front to avoid going out of scope.
Neither finds everything
The freedom that lets AI pentesting agents find something like a complex auth bypass is the same thing that makes them less predictable. They decide where to dig, and that won’t be identical every time.
But pentest results have always varied with the person doing them. It’s why many consultancies will rotate your tester each year – different people find different things on the same application.
This comes down to people’s experience, specialisms, and incidental knowledge.
I’ve found issues because of a blog post or specification I happened to read years earlier and was left sitting in the back of my head. And even the things on a checklist get missed sometimes. People are fallible, and covering an entire application methodically over multiple days can be hard.
Consistent coverage is something we’re actively working to improve, but it’s not something that was ever offered perfectly from anyone. It’s why every human pentest report comes with a disclaimer saying as much, even though most people read it as a formality.
The remediation I couldn’t have written
Every pentester has strong opinions about what makes a good report. I’ve built up many of my own over the years, as have the rest of the current and former pentesters at Intruder. These opinions are what we built our report around.
The executive summary is the hardest page of a report to get right. For a lot of people it’s their entire experience of your work, and at a good consultancy a lot of effort goes into it. We’ve worked to get to the point where the agents are now writing genuinely good ones, and I often read them first on our own reports to get a good overview before diving into detail.
Where they’re still off is scoring risk. They rate things higher than I would, partly because they can’t have the conversations with you that would bring a rating down, and partly because LLMs err towards caution when you start talking about cybersecurity.
That’s the right direction to be wrong in, but it does mean you’ll see things flagged as more serious than they are, and it’s something we’re working on.
Remediation guidance is where the agents excel. After a long week on a pentest it’s tempting for a human to write generic advice, and you catch yourself and try to do better. Unfortunately, you’re usually limited by time and usually by not having the source code.
The agents don’t have either problem, so they’ll tell you the line of code to change, and what to change it to on every issue. They also write about mitigating controls that can help reduce the impact of the issue, or provide a temporary fix.
We’re seeing that speed up how quickly people actually fix things. We had a customer with 78 issues who fixed all of them within a few days, which as a human pentester I have never seen happen. I couldn’t have written the level of detail that made those fixes possible in the time I’d have had.
So is AI pentesting as good as a human?
AI pentesting and human pentesting are different, and once you stop trying to rank them the comparison gets a lot more useful.
The agents are finding serious issues in applications that human testers have already been through multiple times. They’ll read a whole codebase, work through every path in it, and write up every instance of what they find with the fix down to the line of code.
And you can run one whenever you want. For what a single human engagement costs you can run four AI pentests, which means you can do deep testing on a regular basis rather than once a year.
That’s a key thing that is missed by human pentesting, and often applications have undergone a lot of untested changes in between pentests.
See what Intruder’s AI pentester finds in your application. Learn more.
Author
Written by Daniel Thatcher, Security Research Engineer, Intruder
Daniel is a security researcher at Intruder with a background in penetration testing, and a focus on exploiting web applications in black-box scenarios.
Sponsored and written by Intruder.
Tech & AI
Fast RF Device Testing with Analog Sources: A Practical Guide to Amplifier and Receiver Characterization
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RF and microwave systems increasingly depend on amplifiers and receivers that perform reliably across wide frequency ranges and demanding signal conditions. Confirming this performance calls for signal sources with wide output power range, low phase noise, and fast switching speed. Amplifier characterization relies on accurately measuring gain, compression, and intermodulation distortion, while receiver verification often involves simulating blockers, calibrating power accuracy, and substituting for local oscillator and clock circuitry during early design stages. This guide walks through these test applications step by step, showing how engineers and technicians can validate RF and microwave hardware across research, development, and manufacturing environments.
Devyn Johnston has spent years chasing smaller and smaller gaming machines, and this latest effort pushes that habit into territory that feels almost stubborn. Inside the ZCOOI Mugitya FE9, an RTX 5090 Founders Edition sits in a chassis that measures only 56 mm thick across the top and 79 mm at the base, all while still packing a full desktop processor and a 1000-watt power supply. The case arrives as a set of thick CNC-machined aluminum pieces, each finished in black or silver anodizing with a few gray-gold accents on the nameplate and front panel, and the whole collection ships inside its own aluminum carrying case.
Assembly begins by removing the back panel, front cover, and bottom portion, which are all held together with run-of-the-mill screws, so you can just put in the mini-ITX motherboard. Johnston chose an ASUS ROG Strix X870I for this build, which included Wi-Fi 7, a 2.5 gigabit Ethernet port, and a couple of USB 4 ports. He then topped it off with a 64GB of DDR5-6000 RAM, split evenly between the two memory slots, and paired a 2TB Samsung 990 Pro with a second 2TB 980 Pro for a total storage space of 4TB. The power supply is the Corsair SF1000, which is stealthily installed on the bottom panel, and every single cable has been cut to a custom length to ensure that nothing takes up room inside the box.
Acer Nitro V 16S AI Gaming Laptop | AMD Ryzen 7 260 Processor | NVIDIA GeForce RTX 5060 Laptop GPU…
- AI-Powered Performance: The AMD Ryzen 7 260 CPU powers the Nitro V 16S, offering up to 38 AI Overall TOPS to deliver cutting-edge performance for…
- Game Changer: Powered by NVIDIA Blackwell architecture, GeForce RTX 5060 Laptop GPU unlocks the game changing realism of full ray tracing. Equipped…
- Supreme Speed. Superior Visuals. Powered by AI: DLSS is a revolutionary suite of neural rendering technologies that uses AI to boost FPS, reduce…
A Thermalright AXP90-X47 low-profile all copper unit provides cooling and stands only 47 mm tall, comfortably fitting within the 49mm clearance on AMD boards. Johnston liked the orange fan so much that he replaced it with a black Noctua model, which allows you to see the blur of the fan spinning from the side panel without detracting from the overall beauty of the build. The graphics card is attached using a solid PCI-E riser rather than a fragile cable, and to get it to fit properly, Johnston removed the original bracket and bolted it directly to the chassis, allowing the coolers to rest behind matching holes in the panels.
One minor detail turned out to be the standout. The power button is based on a conventional keyboard key, and the case has four removable keycaps in black, white, transparent, and semi-transparent finishes, allowing you to customize it to suit your mood. Once everything is in place, the chassis weighs 6.5 kg and is supported by one of two available foot types, a narrow bar or a broader set for added stability. Magnets hold the side panels and coverings in place, allowing you to open the item with little more than the included aluminum screwdriver.
He put the finished machine through its paces, running The Witcher 3 Remastered at a smooth 120 frames per second on extreme with ray tracing and DLSS Quality turned on. The graphics card maintained a consistent 230-240 watts while staying cool at 54 °C, but the 7800X3D drew 53 to 60 watts and peaked at 70°C. Resident Evil Requiem drove the GPU to around 500 watts and 71-74 °C, but it still managed to maintain frame rates around 130. Cyberpunk at a resolution of 5120 by 2160 with strong ray tracing pushed the GPU to roughly 500 watts and 71-72 °C in the middle of the action, while the CPU reached nearly 80 °C under load. Downsizing to 1440p resulted in the GPU maintaining a very respectable 430-450 watts and 67 °C. The only issue occurred during the initial shader compilation, when the CPU soared beyond 95 °C, but as the game stabilized, the temperature remained within the range of what a low-profile cooler can tolerate.
When the system is up and operating, noise levels average around 43 decibels, with an occasional electrical hum from the power source that refuses to go away completely. The design does not allow for additional case fans to be installed, allowing the graphics card’s own cooler and copper heatsink to handle the work. He also has to consider that this case was designed specifically to house the Founders Edition 5090 (as well as the 5080 FE or PRO 6000), so swapping out the graphics card later for something new would necessitate tearing everything down and rebuilding, or ditching the case entirely. Johnston appears to consider this setup as a beautiful piece of technology to admire rather than a tool to be used on a daily basis. Yes, it achieves high frame rates while taking up less area than a typical tower.
A complete system would cost roughly $465 for just the case, before you add in all of the additional components, and the issue is, every component must fit perfectly or the entire thing will not close. When everything is fully put together and the last panel is clicked into place, you have a slab of aluminum that houses a full 5090 and an 8 core processor, all held together by magnetic panels and requiring only one keyboard key to boot up the system.
Besides running controllers with outdated firmware, the affected stores likely also failed to put the devices behind a VPN and create a restricted list of IP addresses allowed to log in. These protections are standard but often not followed. In this case, that failure enabled a mass hack that likely cost tens or hundreds of millions of euros. Spain, Portugal, Poland, Germany, and Belgium were also reportedly affected.
“Two things would have changed the outcome,” researchers at Pen Test Partners said Wednesday. “Cyber hygiene would have prevented the access: take the controller off the public internet, put remote access behind a VPN and an IP allowlist, bring third-party kit onto the network in a controlled, owned and documented way rather than leaving it on a contractor’s own connection, change known and default credentials, and patch to a current release.”
Researchers from security firm Claroty warned of the threat of ignoring these common-sense steps last October. The US Cybersecurity and Infrastructure Security Agency did the same in February.
In an email, Copeland acknowledged a vulnerability “identified in a discontinued, end-of-life generation of controllers” and said the company is working with customers to prevent similar incidents in the future. Danfoss representatives didn’t immediately respond to questions. CNN said the company also cited discontinued models.
So far, the party behind the attack has remained silent. That raises the possibility that the motive was vandalism on a mass scale. Another possibility is a nation-state or other international actor aiming to create a continent-wide disruption.
The incident underscores the tension between the financial and security departments in most businesses. Some companies are loath to spend large sums of money on new equipment when there are no tangible benefits or savings. Only after a devastating hack do they realize why the investment was worthwhile. Executives at affected supermarket chains will likely learn the hard lessons. Less clear is whether the cautionary tale will sway other businesses.
Enterprise investment in artificial intelligence has, understandably, been concentrated on compute. Access to GPUs, model performance and the cost of inference have become central considerations as organizations move AI workloads from experimentation towards production.
Vice President of Cloud Strategy at Wasabi.
However, an increasing proportion of the economic and operational challenge sits elsewhere in the infrastructure stack. AI depends on data at every stage of its lifecycle: datasets must be collected and prepared, training data must be made available to compute, checkpoints and model artefacts must be retained and information may need to move between infrastructure providers as workloads and commercial priorities change.
As these workloads scale, the storage layer is becoming a strategic component of AI infrastructure with significant implications for the economics of enterprise AI.
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AI is changing the role of storage
Traditional storage strategies have generally been built around considerations such as capacity, availability, performance and retention. AI introduces another dimension: the relationship between storage and the changing requirements of the wider infrastructure environment.
Different stages of an AI workflow have different storage requirements. Data being actively processed may require high-performance infrastructure, while historical datasets, checkpoints and model artefacts may place greater emphasis on durability, security, accessibility and cost. Applying the same storage approach across the entire lifecycle can therefore result in inefficient infrastructure utilization.
The challenge is particularly significant because AI data volumes can be substantial and continue to expand as organizations generate additional training data, model versions and associated artefacts.
The objective should not be to minimize storage expenditure in isolation. It should be to ensure that data is held on infrastructure appropriate to its role, while maintaining the performance, security and accessibility required by the business.
This is the foundation of what can be described as AI storagemaxxing: the systematic optimization of storage across the AI lifecycle to ensure that infrastructure investment is aligned with business and workload requirements.
Data mobility is becoming an infrastructure requirement
Organizations are adopting a broader range of infrastructure providers, including hyperscalers, specialized GPU providers and emerging cloud platforms known as neoclouds. The economics and availability of these environments can change rapidly, creating incentives to move workloads between providers. Compute can be relatively straightforward to reallocate. The associated data is considerably less mobile.
Large training datasets and model artefacts can be expensive and operationally complex to transfer. Where significant charges are associated with moving or retrieving data, organizations may find themselves constrained by infrastructure decisions that were made at an earlier stage of their AI program, creating a form of unintended dependency.
The ability to move data should therefore be considered when designing AI infrastructure, rather than treated as a contingency for a future migration. Open standards and interoperable storage can provide an important degree of separation between data and compute, allowing organizations to adapt their infrastructure strategy as market conditions change.
For technology leaders, this creates a broader consideration than simply assessing the cost of storage capacity. The relevant question is the total economic and strategic cost of retaining data within a particular environment.
Storage optimization must include security
Training datasets, proprietary information and model artefacts can represent significant commercial value. At the same time, the number of applications and automated systems interacting with enterprise data is increasing. Storage resilience must therefore form part of the AI security strategy.
Immutable copies, appropriate access controls and robust recovery processes can help organizations protect critical data from ransomware, accidental deletion and unauthorized modification. These capabilities are particularly important for datasets that would be difficult or expensive to recreate.
Security should consequently be considered alongside performance and cost when determining where AI data resides. A storage environment that reduces expenditure but creates unacceptable recovery or security risks does not represent effective optimization.
Predictable economics will become increasingly important
Storage costs can include capacity, access, retrieval and data transfer. Where these costs are difficult to predict, they can complicate infrastructure planning and influence architectural decisions. This becomes particularly problematic when organizations need to move large datasets between storage and compute environments.
Predictable storage economics allow organizations to make infrastructure decisions based on workload requirements rather than uncertainty over the cost of accessing or moving their data. This distinction will become increasingly important as AI workloads mature. During experimentation, infrastructure decisions can be relatively temporary.
Production workloads, by contrast, may persist for years and generate substantial quantities of associated data, so the storage strategy needs to account for the full lifecycle of that data, rather than the requirements of a single workload or infrastructure deployment.
The strategic role of the data layer
The next phase of enterprise AI adoption will require organisations to look beyond compute optimisation. GPUs will remain critical. Model efficiency will remain important. However, neither can be optimized effectively without considering the data on which AI workloads depend.
AI storagemaxxing provides a useful framework for applying the principle: storage should be treated as part of the AI operating model. Assess storage according to several strategic criteria: whether it is appropriate for the workload, does it provide adequate protection and recovery, are the economics predictable, and could data be moved without friction as infrastructure requirements evolve.
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Back when Googlebooks were first unveiled, Google confirmed that some Chromebooks would eventually get the chance to become Googlebooks. Except one person decided that they didn’t want to wait. Reddit user u/mawitime has successfully booted Googlebook OS on an existing ASUS Chromebook Plus.
This gives us one of the earliest looks at how Google’s new Android-based laptop operating system might behave on current Chromebook hardware. Surprisingly enough, the initial verdict is pretty positive. The user says it runs extremely smoothly and can occasionally seem even faster than ChromeOS. The machine isn’t running some brand-new flagship processor either. It has a 12th Gen Intel Core i5-1235U, 16GB of LPDDR5 memory, and 256GB of UFS 3.1 storage.
Don’t wipe your Chromebook just yet
Keep in mind that this is a homemade solution and not an official installation. The Reddit user described this project as “Frankensteined.” Wi-Fi, sound, and Pixel integration don’t currently work, and getting this far took several days. They relied on Google’s Antigravity coding agent during the process and have since been looking into making the setup more repeatable.
So no, this isn’t secretly an easy way to save yourself from buying a new Googlebook. What it does offer is an interesting proof of concept. Googlebook OS can at least boot and provide a responsive desktop experience on hardware several generations behind the laptops Google is currently selling.
Google has plans to do this properly
Google officially says many of its newest Chromebook devices will be eligible to transition to Googlebook OS, although it hasn’t announced the full compatibility list or consumer migration process. Existing Chromebooks will continue receiving ChromeOS updates through their supported lifetimes, while qualifying newer hardware will eventually get migration options.
Back in May, we reported that Google VP John Maletis discussed letting existing Chromebooks move toward the new Googlebook experience rather than forcing everyone onto brand-new hardware. This unofficial installation doesn’t tell us which models Google will support. It’s also unclear how polished the official transition will be or whether every Googlebook feature will survive the move. Still, the recent experiment shows that Googlebook OS doesn’t need bleeding-edge hardware to run well.
The Soonpho M7 Pan Tilt Head is a useful photography tool for remotely aiming a decently-sized camera. Only, [Kyle Housley] wanted a little more control than was available out of the box. Hence, he built a custom controller that could deliver just that.
The project is based around an ESP32-S3 paired with an NRF24L01 wireless module. The ESP32-S3 is hooked up to a computer to receive commands over serial on what movements to make. It then transmits over the NRF24L01 to another NRF24L01 module that has been hacked into the original M7’s controller. In doing so, it can command the M7 to move to a given specific pan and tilt position, or to make a smooth motion along a vector. It can also trigger shutter release in the attached camera as needed, to capture whatever is being looked at.
[Kyle] has used this rig to create a motion tracking system for aircraft photography. With a Yolo vision model running on a laptop, the pan-tilt head can be commanded to track aircraft passing by and photograph them when they’re in frame. It’s a pretty slick setup; we’d love to see similar done with the aid of ADS-B data, too—perhaps even with automatic image tagging and upload.
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